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piker 3 minutes ago [-]
Having been a law student and practicing lawyer, it's clear to me that law professors aren't really representative of much if any part of private practice. Most of the things they think and reason about are quite theoretical and academic, and it doesn't surprise me that the models would regurgitate a more average response which most human graders would prefer.
That's the entire point, though!
The legal academy is supposed to have outlying opinions on things and present novel philosophical answers to questions. (And questions to answers!) So in addition to the statistical arguments against this paper made elsewhere, to me it doesn't real much new information.
godelski 8 hours ago [-]
I find this study quite suspect. I'd have to dive deeper but there's definitely significant alarm bells that should be going off for anyone reading.
Figure 2 (page 6) screams problems. There's only 16 professors (3k comparisons each?!?!) and the professors are all over the place. That's very high variance, suggesting the study has no meaningful statistical power. Poor instructor 16 can't catch a break lol
There's also really clear bias given that the main results only feature Google models. Other models show up elsewhere, why not there?
I'm no lawyer, but I'm a pretty competent statistician and can confidently say this paper has a smell to it. I can't call it bullshit, but there are red flags all over
Paracompact 5 hours ago [-]
Independent of whether it has any meaning (because the entire paper might be a bit iffy), I find it curious that Instructors 3 and 8 have the lowest harmfulness rates, quite a bit lower than even the LLMs, but not the highest preference rates. Harmfulness anticorrelates with preference, but not perfectly. Some amount of charisma appears to be a factor even in selections by professionals?
RataNova 39 minutes ago [-]
This is exactly why I'd be cautious about interpreting the preference metric too strongly
RataNova 44 minutes ago [-]
Agreed. The study might show something useful, but the headline is doing a lot of work.
esquivalience 4 hours ago [-]
I think your 3k figure comes from here - It is explained:
> As judges, the professors then completed 2,918 blinded, forced-choice comparisons (median per judge: 200), each time indicating which of the two anonymized responses, from the instructor or the LLM, they would rather give to a student
IshKebab 2 hours ago [-]
So did were the answers fact checked? If not that seems like a pretty obvious flaw!
runarberg 7 hours ago [-]
The study was conducted by Stanford’s HAI institute, which receives heavy funding from Google (how much I couldn’t find because they don‘t publish their donations in a place I could find it; but I suspect it is alot). And the authors did not declare a non-conflict of interest at the end of the paper.
keeda 4 hours ago [-]
Wait, where are you seeing the link to HAI? TFA mentions something called "liftlab" which seems to be something under Stanford Law School and separate from HAI. The study has more than a dozen authors from as many different universities but HAI is not mentioned.
tomjakubowski 3 hours ago [-]
The leader of the study, Julian Nyarko, is Associate Director and Senior Fellow at HAI. I can't say whether that means the study was conducted by HAI, but there is at least a connection to it. https://hai.stanford.edu/people/julian-nyarko
gguncth 2 hours ago [-]
Sure, but in two years AI has gone from “impressive tool, but not a replacement for knowledge workers” to “the study where it beats our highest caliber of knowledge workers may have some methodological deficits.” In another two years it’s going to be curtains.
wouldbecouldbe 46 minutes ago [-]
The issue is, it almost always outperforms knowledge workers.
IF the right questions are asked, and IF steered into and corrected at a few crucial points. IF not it goes off in the wrong direction really quick and that's a problem that's still mostly unsolved in the last 2 years.
And that can be catastrophic in high risk environments, like legal, medical or high risk software products where being wrong in the wrong place can mean bankruptcy or even cost a life.
I have a few marketing website where I let the CEO's run crazy with Claude cowork, they are making PR's like a madman, but they are not allowed to touch any of the API's & platforms where there is real user data & sensitive information.
goolz 41 minutes ago [-]
Ya, while the tools are really solid and have seen huge leaps these past two years, in no way will an LLM be able to do any of it unguided in two years. Just a humble opinion that I would love to see be wrong.
wouldbecouldbe 35 minutes ago [-]
Yeah it can do things unguided if the tests to confirm its correctness are very solid. Thats where a lot of progress has been made and where agents are good, but this is domain specific, and a chance where startups can shine.
amelius 22 minutes ago [-]
> Sure, but in two years AI has gone from “impressive tool, but not a replacement for knowledge workers” to “the study where it beats our highest caliber of knowledge workers may have some methodological deficits.”
With that kind of logic ... anything is possible.
AlecSchueler 36 minutes ago [-]
> the study where it beats our highest caliber of knowledge workers may have some methodological deficits
The point is that if the study can't validate the claims being made then we can't actually extrapolate from that claim.
Forgeties79 1 hours ago [-]
Assuming it keeps improving at the same rate, which I think we are already seeing not play out. If you compare the first six months when GPT truly hit the mainstream to the previous six months, the improvements are not nearly as evident. That isn’t to say they aren’t noticeable, I could definitely tell it’s improving, but not nearly at the pace it once was.
There’s also the fact that they can’t possibly keep improving frontier models at the same rate (I.e. training investment) when investment starts slowing down. The amount of cash being burned is completely unsustainable and you’re already seeing some pullback.
byzantinegene 46 minutes ago [-]
it's also worth keeping in mind that alot of the 'improvements' are actually advancements in harnesses and tools.
nopurpose 53 minutes ago [-]
On the other hand we keep seeing only marginal generational imorovements in CPU space, yet performance gains over last 10 years in CPUs are very material.
Every new model might not be a leap like it used to be, but give it enough time and improvements add up.
Forgeties79 7 minutes ago [-]
Nobody is disputing that. I specifically said that I can see the improvements from the last six months. What I’m saying is we can’t assume that every two years it will improve at the same rate.
The further we get into this, the more AI feels like 3-D printing. Significantly bigger and will be more widely used for sure. But nowhere near the “new industrial revolution” that all these companies are making it out to be
ALittleLight 7 hours ago [-]
The paper says the professors have a median of 200 comparisons each. It also says they only used 2 models because using more models would require more comparisons and they selected Google models because Google was branded/advertised as being education focused. When you see other models show up elsewhere, that's because they extended the main idea to other models but using LLMs to judge instead of human professors.
godelski 7 hours ago [-]
Sure, but the biggest problem is they have no statistical significance. Variance is too high. How do you distinguish the signal from the noise? Confidence intervals aren't enough.
But is it a surprise law professors aren't great statisticians?
Certhas 3 hours ago [-]
I disagree. 16 isn't necessarily the relevant N here but the number of responses is.
If you have 100 responses from 1 professor, and the AI wins 75% of the time that is very likely a true signal that the AI is better than this prof. It would be incorrect to generalize this to all profs though.
Further, if you sample 16 profs and the AI beats 10 of them you can be fairly certain that the real percentage of profs it beats isn't 10%. Further, when estimating the probability that the AI beats a random prof, it's the relative estimation error that scales with 1/sqrt N. If you have a coin and it lands heads up 16 times, that tells you something quite robust about the coin.
Reasonably estimating confidence intervals at small N and high p is not trivial. But it can be done.
A good heuristic is "add 2 successes and 2 failures" which is due to Agresti & Couli.
But does it really matter? It seems fairly obvious that AI is going to outperform professors. While the studies run, there are three more model releases that change the calculus entirely. I wonder how much we are learning with these studies about what is going on.
greggoB 2 hours ago [-]
> I wonder how much we are learning with these studies about what is going on.
So your alternative is to not have any studies and everyone can just stump up anecdata as "evidence" for the capabilities of these models?
jstummbillig 7 minutes ago [-]
Doing things that are well meaning, but ineffective is not great policy. The simplest alternative to doing things that don't work is always not doing them. Better ideas are of course welcome, but not required.
master-lincoln 2 hours ago [-]
it sounds like you are saying science doesn't matter but your feelings do
suddenlybananas 2 hours ago [-]
Does it matter if a study is fraudulent or incompetent? Yes.
zeristor 2 hours ago [-]
That is the assumed narrative; however it shouldn’t bias any evidence.
ulrischa 31 minutes ago [-]
By its very nature, the field of law is ideally suited for AI language models. Fundamentally, everything is based on interconnected texts. I believe that even larger waves of layoffs could loom here than in the IT sector. However, it is likely that a more powerful lobby will be at work here—one that will grossly inflate the perceived value of their work and shield it from outside intrusion.
causal 10 hours ago [-]
As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others.
I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
qingcharles 4 hours ago [-]
I've used general purpose LLM AI (e.g. run-of-the-mill Claude, GPT etc) heavily to draft legal documents. The biggest trap is the hallucinated citation. It will easily insert an absolutely authentic sounding quotation from another case that perfectly proves the point you are trying to make, then it'll make up an authentic name for it, e.g. United States v. Shenzhou Electronics Inc or whatever. You can get really comfortable after checking its output a few times and getting no false citations, and then BAM, it'll put three in the next motion it writes.
Any lawyer who isn't using LLMs for research is behind the curve, though. They are unbelievable at finding niche cases you would never have found on your own. Previously it was a lot of exact search term matching, which is inherently useless for a lot of legal research. I need something that can search on vaguer terms, which AI can do incredibly well. Just check the results. I'm sure the LLMs from Lexis Nexis/Westlaw are probably better than the general purpose ones.
LLMs make fantastic paralegals. If you're doing any legal work, you should be using it, even if it's just to shoot ideas at. Have it play devil's advocate. My friend always has it play the other party's lawyer to see what all the counter-arguments are going to be.
Just like you would with software development. If you care about what you are creating, CHECK THE OUTPUT.
em500 4 hours ago [-]
> The biggest trap is the hallucinated citation. It will easily insert an absolutely authentic sounding quotation from another case that perfectly proves the point you are trying to make, then it'll make up an authentic name for it, e.g. United States v. Shenzhou Electronics Inc or whatever.
Naive question from an outsider: aren't there searchable databases of cases (with complete text) so that citations could be checked automatically, either by the same or an independent agent?
timpera 3 hours ago [-]
It depends on the jurisdiction. I'm based in France and all cases here are now freely available online to people and agents [1], but it's very recent for lower courts. However, I recently had to work on Texas case law and we had to purchase access to a (very expensive [2]) database since most of it wasn't public.
It’s a band aid solution because the model can get stuck in a refutation loop, where it argues a point by pulling up a contradicting source ad infinitum. The holy grail, which has not been yet reached, is figuring out how to dynamically align the model to be consistent with all the sources in the first place (and this is a problem of provenance rather than model design)
thenickdude 19 minutes ago [-]
>The biggest trap is the hallucinated citation
The "biggest problem" being the one thing that is trivial to verify against concrete databases is a bit convenient don't you think?
I think it's more likely that it makes mistakes evenly but the one thing that you are able to check with certainty is the only place you discover the errors.
RataNova 35 minutes ago [-]
I think the paralegal analogy is right, but with one important difference: a human paralegal usually knows when they are unsure, or at least can be trained to flag uncertainty
BartjeD 3 hours ago [-]
A legal professional can be personally liable for not finding the most recent case-law.
The knowledge cut off gap means the models sometimes don't know about the most recent case-law, in a given situation.
I've seent his happen multiple times now. Accountants and legal professionals advising clients based on outdated information assembled through chat-gtp, claude and copilot.
Professionals drafting letters and missing recent case-law which handles their exact case. It's unreliable.So it can save you some work; but it can't save you all of the work. And in some cases its mistakes really force you to redo all the work, and more, to be thorough and have confidence in the result.
lukan 28 minutes ago [-]
"The knowledge cut off gap means the models sometimes don't know about the most recent case-law, in a given situation."
But they can perform live websearches or go directly to a DB specified.
timpera 3 hours ago [-]
You definitely want your AI to search legal databases, and not draw from "memory". This is where AI offerings from Thomson or Lexis could shine, especially in jurisdictions where case law is not freely available online.
eunos 3 hours ago [-]
Seems companies like Thomson Reuters or other legal services have incentive to build LLM with RAG over legal cases texts and robust hallucinations detection on reference
thewebguyd 10 hours ago [-]
I think this is probably true for most skilled professions. AI is best used in the hands of folks already knowledgeable in the skills/professions they are using it for.
I liken it to me googling things as a sysadmin vs. Jane from accounting doing it. The non-tech end user is far more likely to make the problem worse, or install something sketchy from the ad riddled results than I am, or one of my help desk employees are.
I wouldn't trust myself to draft an important legal document using AI without the advice of a lawyer, much like I wouldn't really want to rely on my lawyer to use AI to write code for me.
godelski 9 hours ago [-]
> I think this is probably true for most skilled professions.
I agree, BUT I also find that it's easy for experts to atrophy quickly. When the AI is right 80/90% of the time it lulls you into over confidence.
I find those that are best and make the greatest use are the ones who remain skeptical but also use the tool. The same people who were already nuanced and picky before AI. The same people who already doubted and questioned their own work, and used that suspicion to help prevent them from having over confidence in their own work. If you weren't willing to just "lgtm" with your own code, it's difficult to do that with AI.
(To be clear, I'm not saying perfectionists. Some might call them that because the picky people have higher standards, but a good expert has to also understand that perfection doesn't exist. That's often a driving force in the suspicion! This also tends to cause them to continually improve)
stult 8 hours ago [-]
I would agree with this point and as I explained in a comment replying to the GP comment above, that atrophy is far more dangerous in the legal field than it is with code because legal documents do not benefit from the structural safeguards available for code, like automated testing, static typing, static analysis tools, etc. IME with legal LLMs so far, they are easily in that most dangerous valley where they can lull you into a false sense of security while still introducing extremely dangerous mistakes that are frequently difficult to detect without very careful reading.
The danger of those mistakes creeping in also grows exponentially the farther a lawyer strays from their core legal expertise. There are a few statutes I know inside and out, and I can spot LLM analytical errors related to them in a split second, but once I venture out into domains where I am not an expert (but where I am nevertheless reasonably qualified to practice), it becomes much harder to spot drafting mistakes because I have not refreshed my own understanding of the law by reviewing the relevant cases or statutes as I would when drafting the analysis myself from scratch.
ChrisMarshallNY 9 hours ago [-]
> I wouldn't really want to rely on my lawyer to use AI to write code for me.
Yet that is exactly what a lot of C-Suiters (many of whom are lawyers), are doing.
xiaoyu2006 9 hours ago [-]
Vice versa there is also a lot of irresponsible programmers doing stupid things with ai. Irresponsible people stay irresponsible, AI just make them more productive at being irresponsible.
consp 4 hours ago [-]
The problem is the low levels have no influence whatsoever. The higher ups force crap down and none ever comes back.
zuzululu 9 hours ago [-]
im not so sure
i think devs overestimate their own role and underestimate others
i am seeing lawyers and doctors roll out their own software with AI
but we dont have their training and experience
causal 2 minutes ago [-]
Also worth remembering that LLMs have jagged intelligence. They are probably better software developers than anything. Is there a complement to Gell Mann Amnesia- where you assume it’s good at other jobs because it’s good at yours?
thatcat 9 hours ago [-]
So a software engineer could diagnose an illness with ai, even if they happen to be right that doesn't really prove much about how bad of an idea it could be in a long tail scenario.
stackghost 9 hours ago [-]
It's like that in engineering, for sure. My background is in aerospace and there are lots of things that a reasonably technically-inclined random can probably do passably. It takes an engineer to know which tasks those are, though.
I would imagine it's similar in law, in that it takes a lawyer or judge to know where the foot guns lie.
Merad 8 hours ago [-]
> Some things seem harmless, i.e. drafting a will
Absolutely not harmless if you're the executor of an estate forced to deal with a screwed up AI will. I just handler my dad's estate this spring. It's a frustrating and confusing process even with the simplest of estates.
b40d-48b2-979e 7 hours ago [-]
Most people don't have anything that could even be called an "estate".
jcranmer 7 hours ago [-]
Judging from reported figures, roughly 80-90% of households in the US [1] have a household net worth of at least $0. That means that most people do in fact have an estate.
Median household net worth is in fact somewhere in the $100k-200k range, which is definitely something that could be meaningfully called an "estate." (Most of this tends to be the house, the median net equity in which is about $190k as of 2022).
[1] This doesn't mean "homeowners," rather it's a recognition that assets for married or cohabitating couples are usually commingled.
acdha 7 hours ago [-]
It’s just the legal term. If you have a relative die with a bit of stuff and an ancient car, they have an estate and someone needs to execute it even if the total value is less than most lawyers care about.
nocoiner 7 hours ago [-]
Everyone has an estate. Only thing is that you have to die first.
toss1 7 hours ago [-]
Ummm, not quite.
An "estate" is a legal term for property, assets, and liabilities a person leaves behind upon their death. A family member is a top practitioner in the field of estate planning and resolution, and some of the messiest estates they have handled are pro-bono cases of exactly the type of people you would put in italicized "most people": poor, not really able to upkeep a house they inherited from a relative which hadn't had title properly transferred on a previous death because they didn't have money for an attny, now can't get a loan to fix the roof...
Yeah, if you are homeless, carless, and have only the clothes on your back and a shopping cart of stuff, you don't have an estate. Everyone in the middle class in the US has an estate. Much of the time it passes automatically to their spouse on death, but it's still an estate.
And if you are concerned about where it goes, get a GOOD attny. There are many bad ones hanging out their shingle as "Trust & Estate" attnys, and some of the next messiest cases are fixing problems made by those not-so-good attnys.
And NO, AI is not good enough.
stult 8 hours ago [-]
IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sandboxing.
The time lag between drafting and "deployment" also makes for much less effective, much more expensive debugging loops. You can deploy your code to prod in seconds, see an error pop up in the logs, and immediately start debugging. But it will take at a minimum days and frequently as long as several years before an error in a contract or a court filing will be detected, and often the error is beyond correction at that point. Thus, the errors are both more difficult to detect and to resolve.
And the consequences of error are often much greater, both because they are not correctable and because a legal error may risk someone's life, liberty, or substantial property. Although that's not categorically the case, obviously bugs in certain safety critical systems can be as bad or even worse than legal mistakes. But in general, most software is lower stakes than most legal writing.
On the flip side, LLMs do seem to do a better job with basic style and structure for legal documents compared to code. Things like following IRAC format, citing assertions of law (although hallucination remains an issue), and writing comprehensible sentences. These would be the equivalents in code to best practices like good comments, cohesion, consistent use of design patterns, test coverage, clear variable names, DRY, etc. Although the better performance on those more qualitative metrics may just be because even the longest legal documents are typically simpler in structure and have fewer lines of text than a large, complex codebase. Or maybe it's because LLMs are trained on natural language text more than on code. Or because natural language is more forgiving than code, in that minor variation in diction or grammar is unlikely to have any significant effect on how the document is interpreted, whereas even single character errors in code can have enormous effects.
calvinmorrison 8 hours ago [-]
Well this is largely the fault of law itself. especially english style law. A legal, parseable code, in which not every single tiny municipality (some less than 1 square mile) has their own set of rules and laws, not all published or available - but which citizens are expected to abide by of course - how could we expect AI to do well and not some typical TV southern lawyer who knows the judge?
stult 8 hours ago [-]
I could not agree more. A simple example: it boggles my mind how every state organizes their statutes in entirely dissimilar ways. I'm not sure there's a need for every state to have slightly different wording for a murder statute in the first place, but even assuming there is, why do they all have to be scattered around in different code sections instead of every state just following some consistent convention like always putting the murder statute at Title V, Section 1.4 (or whatever the case may be, that's just a random invented example).
For murder that's not such a huge deal because the statutes are typically easy to track down and don't really differ all that much substantively, but once you get really into the weeds on something like commercial contracts it can be a huge pain to do cross-jurisdictional research.
And that's just a tiny, super obvious example of how impenetrable statutory law is, which isn't even the really pernicious problem. Case law is infinitely worse. It makes me absolutely furious how difficult legal research still is. The Westlaw/LexisNexis duopoly is a moral crime and wildly destructive to the quality of government in this country. Every single written court opinion should be publicly available for free on the internet in an easily searched format. It would cost practically nothing to achieve. We're talking about less text than Wikipedia hosts. Yet still many states make it almost impossible to access case law. Even though these cases are law. Binding law that we are supposed to follow, yet we cannot even easily access. It's insane, and largely perpetuated by the complacency of lawyers who can charge others for what should be free, the lobbying of the duopoly, and the incompetence of politicians.
If all of the laws were consistently available and stored in reasonable, consistent citation formats (I would settle for hyperlinking as a replacement for the rat's nest of wildly varying jurisdiction-specific citation systems), it would even be possible to introduce a form of unit testing for legal drafting that would allow us to automatically verify if the LLM hallucinated a citation.
It also doesn't help that we (for what were at the time very good reasons) moved away from the system of legal writs that used to provide fairly standardized, almost "cut and paste" templates for legal filings. So now every legal document (filings, memos, contracts, court opinions, statutes) is drafted like a bespoke, artisanal creation with few strict structural or stylistic conventions. That makes automated interpretation much harder than it needs to be.
RataNova 37 minutes ago [-]
I think that's the right intuition. Legal AI feels especially dangerous because the output can look competent while hiding jurisdiction-specific footguns
rayiner 10 hours ago [-]
I would think that LLMs would be better at avoiding foot-guns. That’s a situation where you have a list of well known rules and potential pit falls, and the work of the lawyer is to apply those to a fact pattern. That’s something that has been hard to automate programmatically, because the fact patterns are similar but different. LLMs, however, seem to excel at applying general principles to differing fact patterns.
atmavatar 9 hours ago [-]
Instead, the LLMs create entirely new foot guns like citing non-existent cases. You can't go more than a week without encountering another news report of a lawyer submitting an AI-generated legal brief rife with bogus case citations, which even includes briefs submitted to state supreme courts.
I would categorize this in the "expertise that people internalize but never figure out how to verbalize" department, and that is a department we have no way to teach an LLM because if nobody is writing out those unspoken, subconscious rules then the LLM has nothing to read about them in its training data.
visarga 3 hours ago [-]
> and that is a department we have no way to teach an LLM because if nobody is writing out those unspoken, subconscious rules then the LLM has nothing to read about them in its training data.
I think on the contrary, LLM providers accumulate huge logs of interaction with their users, which elicit that tacit knowledge and mine it and humans cooperate willingly in order to solve their tasks. Just imagine the corpus of sessions for scientific research, education or software development, it is probably the largest such collection ever to exist. Trillions of HITL tokens per day flow into those logs, carrying our perspectives, choices, original ideas and tacit knowledge. I call this the "human-AI experience flywheel". It's the new stackoverflow, next model generation is based on interaction data from previous one.
My favorite example of this is knowing how to untangle a big pile of cables. There are robots now which can untie a single knotted cable, but I don't think any can do a pile of cables yet. https://www.youtube.com/watch?v=vp-94rsherE
galaxyLogic 8 hours ago [-]
Good point. Same probably applies to code as well, coders much tell us why they write the cde the way they did. And if they have comments in their code, those are highly untrustworthy because noboy fixes comments if the code works.
goodmythical 9 hours ago [-]
I don't know the source off hand, but I've seen llms hallucinating case citations in order to "prove" their premises.
can't get more foot gun than "well according to [fiction] it is a well established practice (that the defendent is guilty)"
dylan604 9 hours ago [-]
But can an LLM come up with questions like what the definition of is is? Seems to me there's a lot of "depends on how you read it" type of stuff that lawyers excel at finding novel interpretations. So what coders thinking of as rules are much less straight forward to understand when it comes to laws
rayiner 9 hours ago [-]
I think that’s a different task than the one OP is referring to. To your example, I’m not familiar with the capability of LLMs in that regard. I have struggled with using the AI features of westlaw when it comes to that sort of argument. (Basically, making an argument that strays from typical route, because that’s the position you happen to find yourself representing.)
_heimdall 9 hours ago [-]
I wouldn't consider drafting a will to be harmless. If its done poorly the next of kin could have to deal with a huge headache and potentially months or years of probate proceedings.
grogenaut 6 hours ago [-]
I had a very well crafted will from my parents, one of whom was a very good lawyer hiring other good lawyers. It was still a pain in the ass for many of the reasons they were trying to make it easy for us.
One thing I learned, just bite the bullet and re-write the whole fucking will instead of making riders.
Piecing the will together from riders was terrible. Al the clauses fell away everyone got older. The final will could have been 8 pretty clear pages.
The other part that is hard is just knowing all of the things that happen with assets and a passing. Luckily we had another lawyer and financial folks to advise us. It was still a lot and not that easy to find details. This was pre-ai that would have helped walk through his shit.
xmcp123 8 hours ago [-]
I think that's actually a perfect analogy to AI writing code.
Drafting a will seems like not a big deal, until that will is accepted as "good enough" and is then in court and under fire.
teiferer 5 hours ago [-]
> drafting a will
Such a document may not make a difference to the person that eventually will have died, but it can make or break the life of generations to come in countries that are so heavily optimized for dynasty building like the US.
conception 6 hours ago [-]
This is why I can’t see how college grads are going to survive the AI apocalypse. domain experts driving LLMs are super powerful because they can spot where they make mistakes. Juniors don’t have that insight and the LLMs then cost them productivity.
geraneum 5 hours ago [-]
> domain experts driving LLMs are super powerful because they can spot where they make mistakes
I don’t know if that’ll be true for long. I just had my colleague who’s a very competent engineer IMO hand me a frontier model vibed PR to review (after reviewing it himself, he claims) which contained random variable assignments, conditionals that do nothing, etc. He’d never do such a thing before. People become too comfortable and get confirmation bias as well.
knollimar 10 hours ago [-]
I'm afraid since claude cheats in benches, what will it do with law?
dgellow 4 hours ago [-]
The same in every other domains. It’s happening now, not in a future tense
datsci_est_2015 8 hours ago [-]
Hmm, what’s the law equivalent of using docker to bypass sudo?
knollimar 2 hours ago [-]
can you make really convincing but flawed arguments that are historically able to win despite competent opposition?
godelski 8 hours ago [-]
Cheat.
Or worse, use historical data to determine the laws of today.
prpl 10 hours ago [-]
there’s really no limit to how many times and ways you can review something with AI, except dollars.
Boss0565 10 hours ago [-]
cannot IMAGINE letting ai write my will rn.
jay_kyburz 9 hours ago [-]
I imagine it's really hard to spot a comma in the wrong place, or a missing sentence in a 10 page contract unless you wrote it yourself, or you assembled it from some battle tested templates.
pojzon 5 hours ago [-]
To give you some example of what can happen if you use AI in legal battle you can look at Valve vs Rothchild case [1].
As others pointed. It kind implies it surpasses professors, but reading more carefully it seems more like the mythos situation. There was a single professor or test that it surpasses.
Reading it makes me extremely suspicious on how cherry picked this was
finnborge 8 hours ago [-]
I understand why the conversation on this article looks like it does, but the study is specifically focused on the potential for LLMs to operate as tutors for law students. I enjoy the extrapolation out to whether LLMs will replace lawyers, but did not find that to be discussed in the study itself.
In the framing of using LLMs as legal tutors, with the implication of lowering the cost of legal training, this seems like a socially-positive outcome. Furthermore, it feels kind of intuitive to me that any contemporary system operating with an LLM and access to legal reference material will be prepared to answer _student-originated questions_ comprehensively and with breadcrumbs or direct references to educational/source materials, as seems to have been found in the study.
The authors explicitly and intentionally emphasize that many legal questions require contextualization, as opposed to some discrete calculated answer. The result of the study implies that the LLM-based systems were capable of using what many of us here understand to be the "stochastic best-fit algorithmic generation" of a contemporary language model to adequately contextualize a student's question, providing insight into the trade-offs or complications implicit in the question, while then, critically, _meeting the professional standards of legal educators in explaining that complexity to a student_.
Realistically, I would hope this provides some confidence to readers of HN that they can actually ask a legal question to an LLM and expect the response will explain the complexity of the law in relation to the question. This is great news, and is likely the minimal pre-work any of us should do before actually consulting a lawyer, if time permits.
On the other hand, I do _not_ think that this study provides any indication that an LLM is prepared to actually provide direct legal counsel. Possibly in the same way that a legal textbook does not replace legal counsel, or perhaps more accurately, the same way that stumbling upon a legal case study for approximately the same situation you're in doesn't guarantee you'll have the same result.
quantisan 8 hours ago [-]
I'm surprised Stanford Law would go along with this over-reaching press release title. How about "For common first-year contracts-law questions, law professors preferred AI-generated answers to professor-generated answers"
mchl-mumo 5 hours ago [-]
The revised title is spot on. It's odd to me how academics are trying to sound like top research labs' CEOs trying to pump valuations by overreaching claims.
TrackerFF 53 minutes ago [-]
In many (most?) countries you can defend yourself, waive your court appointed attorney. You are of course highly discouraged to do so. But sometimes people do it, mostly for smaller claims where they don't want to rack up legal bills for things which might cost more than what is at stake.
But, it makes me wonder, will clients be able to use these AI-attorney systems in the future, in the court. Where they basically either just parrot what the model is instructing them to do, or - I dunno - give the model permission to speak for them (while waiving liabilities).
I have no doubt that some complex AI system can perform better than a bottom-tier, overworked lawyer.
bonesss 29 minutes ago [-]
Pro se litigants are hyper vulnerable to LLM hallucinations.
One wrong advice clump and, like a step onto the wrong path while hiking, all subsequent steps go in the wrong direction. And sycophancy tuning means marginal one-sides takes get presented as sure-fire things.
I’m of the opinion that the big wins aren’t in using the LLMs to do the work (legal, in this case), but rather to refine and improve the dialog and presentation from all parties. A court-centric LLM that could give likely procedural needs to a litigant, and a law-firm-centric LLM could help a pro se litigant create a meaningful and refined set of questions for lawyer consideration, condensed and targeted, saving all parties time and confusion while meeting the clients linguistic needs ‘where they are’.
All the lawyers know things LLMs never will, the law is interpreted, and the written part isn’t engineering grade facts but suggestions interpreted in context. Arguably this is a racket and a thin veneer of plausible deniability for authoritarian rule. But as the law stands even with federal statues and citations from the courts website, practicing lawyers will frequently end up explaining that in this county/country/court/jurisdiction The Way of Things is different.
chewbacha 9 hours ago [-]
My best guess is that Gemini was trained on the textbooks that the questions are meant to test against, thus they are probably better at explicit recall of those questions or related questions.
This is a pretty limited introductory course based on what it says in the methods of the paper itself.
runarberg 9 hours ago [-]
That and the research is done by Stanford’s HAI institute with an obvious bias and the paper is curiously missing a conflict of interest statement.
EDIT: just found out that Google is a major donor to HAI. So this research is at least partially funded by Google. Which is probably the reason the authors fail to declare no conflict of interest.
3 hours ago [-]
dguest 32 minutes ago [-]
I'm not a lawyer, I program.
My understanding is that Civil Law (most of the world excluding UK, US, AU) is like a program: you feed it a situation, it outputs a decision, every once in a while you edit it.
Common Law (UK, US) isn't really a program, but you could stretch and say it's a state machine that has been running since the country started. Every interaction sets a new precedent and changes the state. But the programming analogy falls apart because no one in the right mind would design such a program.
LLMs might actually be the best example of such a program though: Common Law is basically one long chat with an LLM, hundreds of years long.
Before LLMs came along, a Common Law system seemed to have a finite time limit before it's co-opted by wealthy people with the resources to read the whole history. Now I think maybe can push it a bit further.
But it's still a terrible program.
applicative 8 hours ago [-]
What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated statement.
U4E4 8 hours ago [-]
This is true in the naive case.
There are however LLM context building techniques that anchor completions in data structures that persist the structure of claims that support the conclusion contained in a completion. Lots of different patterns exist —organizing logic in language is a rich domain— but the one I’ve liked the most is something called a Claim Dependency Graph that models the relationships between atomic claims as graph edges.
There’s a whole suite of operations you can perform on these structures, and “reconstruct how you came to this conclusion” is absolutely one of them.
mdlman 7 hours ago [-]
I’d love to read more about these type of patterns. Do you have any recommendations?
xattt 8 hours ago [-]
A human has a motive that exists that frames the thought being expressed. An LLM is going to be creating a “de novo” thought in response to a line of questioning.
ashdksnndck 8 hours ago [-]
Same is probably true of humans. In a conversation, we often respond from instinct, then work backwards to a rationalization only when asked. For more considered thoughts, if we’re lucky, we can remember our “reasoning traces” but that’s as deep as our introspection goes. Unless we’re neuroscientists, we don’t even know how many neurons we have, let alone have any understanding of how they generate our thoughts. Motivated reasoning impairs our introspection further, and then dishonesty and communication errors prevent us from relaying the limited remaining information to each other.
Model interpretability work has advanced a lot. Arguably we already can explain AI decision-making better than human brains.
applicative 8 hours ago [-]
No, it happens in the immediate context, where e.g. we say 'No I meant Meredith Jones, not Meredith Smith'- and the possibility of this elaboration is actually part of ordinary communication. I did mean Meredith Jones, not Meredith Smith - thus the use of the past tense The LLM will just give the best answer for what one might have meant, completely reopening calculation.
Nonsense, some of my friends are lawyers and they're able to give you consistent interpretations on why they think about a certain aspect of a law a certain way.
The whole thing is that they work with this the entire time, so they have a really consistent 'head model' of how things work and why and how considerations should be weighted/ordered/whatever. LLMs just do not have this, there's no consistent underlying reasoning (the 'reasoning' traces in LLMs are really inconsistent)
j45 8 hours ago [-]
LLMs hallucinate, because humans hallucinate.
Asking the LLM in a way where it annotates its sources, it can greatly increase the pattern matching to closely simulate logic, just like in humans.
I understand the question of why did you say this, not that, I have seen other ways of asking that which do not seem to trigger the LLMs over-response in the other direction.
latentsea 8 hours ago [-]
Humans hallucinate because they take shrooms or have schizophrenia.
applicative 8 hours ago [-]
No, the hallucination of its reasons follows immediately from the technique of probabilistic inference. You can see this in real time, just ask 'why did you use this word, not that word?' It is in the position of a desperate liar. All its responses are essentially 'rationalizations'
rockskon 7 hours ago [-]
I do question at what point AI could be useful as a teaching aid.
The quality of LLMs depends heavily on, among other things, how you word your questions.
Knowing the correct questions to ask is not something most students know how to do given that it tends to require a fair bit of pre-existing domain knowledge.
aitchnyu 58 minutes ago [-]
Tangential, is there a "test suite/CI" for AI writing legal documents? Long back in terms of AI progress, a lawyer filed something with hallucinated sources. Do new tools prevent this?
RataNova 1 hours ago [-]
I'd read this less as "AI replaces law professors" and more as "AI may be a surprisingly strong first-pass tutor, especially when the student knows enough to question it"
weatherlite 2 hours ago [-]
It is important for society to understand it is not merely programmers and customer support who are at risk of losing their jobs. Clearly A.I can do much more than just program.
throw7 9 hours ago [-]
Oh, a "Human-Cented" study by AI lover:
Julian Nyarko
Professor of Law
Co-Chair Stanford Law AI Initiative
Senior Fellow, Stanford Institute for Human-Cented AI (HAI)
LOL!
iLoveOncall 40 minutes ago [-]
The title of the study "Law Professors Prefer AI Over Peer Answers" is VERY different from the title on HackerNews. This is completely clickbait at this point.
epicureanideal 7 hours ago [-]
One way to make legal services more affordable and accessible would be to put the burden of ensuring the AI legal services are accurate on a private-public partnership with the government.
If a person using the service is given inaccurate legal advice and acts on that advice, the person can't be charged with a crime, can't be given any civil penalties, etc., as long as the law in question is non-obvious.
Obviously if by some exploit, some fundamentally obvious crime (murder, theft, obvious fraud, etc.) is said to be legal, that wouldn't apply, but of course the service should try to prevent those kinds of exploits anyway.
Could limit this to something like business regulations to begin with, or even specifically for small businesses, or contracts within some time limit and dollar amount that would otherwise be coverable by small claims court, etc.
mchl-mumo 5 hours ago [-]
16 is such a small number for what they phrase as an important finding. It really couldn't be much harder to coordinate with 100+ professors.
elnatro 4 hours ago [-]
When I see news pieces like this I wonder about the failures. Maybe the failure percentage is low but what happens if a bot gives bad counseling? Who is responsible then?
Attorneys will be using LLMs for convenience but they will not disappear, because there needs to be an ultimately human responsible of the decisions.
tipsytoad 3 hours ago [-]
Curious how they do a “blind” preference test. To any evaluator I’m sure it’s quite clear which answer is AI vs human.
galaxyLogic 8 hours ago [-]
I'm going to need some legal help for my startup. But I can't pay much. So I figured I will ask AI all relevant questions, as well as forms filled etc. Perhaps even create a patent-application for me.
THEN I find a human lawyer and give AI's answers to them and say "Can you find any errors in this? Can you improve it?" .
That way I think my legal bills should be smaller because the AI has already done most of the work. What do you think? Which LLM is best for legal work?
apparent 6 hours ago [-]
I think that within a few years, most lawyers will expect that clients will have run contracts through an LLM prior to sending them to outside counsel. Emails will be along the lines of:
Please see attached contract we received from [counterparty]. ChatGPT says blah, blah and blah should be revised. What do you think? Is there anything else that we should change?
galaxyLogic 6 hours ago [-]
Right. That will reduce workload for the lawyers. But will their fees then go down? I'm kinda worried that if I don't give them the LLM produced legal docs for review they will just use the LLM themselves and then charge me for the work the LLM did :-)
It's bit like with doctors, you'll want a second opinion, if you can afford it.
apparent 5 hours ago [-]
TBD. Probably depends on whether what you're paying for is access to their lawyer-level LLM, which they would run it through, or for actual expertise.
Probably for important deals, detailed human review will be expected.
Maybe the real value-add will be the insertion of language that LLMs won't be able to figure out, but which will be favorable for the side that inserted them.
dlahoda 8 hours ago [-]
i use codex to do initial research and draft texts (in typst). i use files-output skill so that all research contexts are rendered into files md files.
i do second phase on codex, by asking to download all pdfs and extract all text of laws it references. can repeat fully local research step.
after i ask gemini to find issues and criticize.
UPDATE: there many legal skills on github to try, not used so any yet
galaxyLogic 8 hours ago [-]
Are you a lawyer yourself?
SomaticPirate 8 hours ago [-]
[flagged]
KnuthIsGod 9 hours ago [-]
In the hands of a domain expert, AI is useful.
In the hands of the naive, it is a foot gun.
I killed my Arch installation and was stuck at the GRUB prompt.Unwilling to brush up my rusty knowledge of GRUB syntax, I asked Gemini for help. The commands Gemini suggested would have wiped my hd...
Once Gemini was told that I was using BTRFS, the suggestion from Gemini looked a bit more sane, but still looked incorrect to me.
It was only after I informed Gemini that I was using a NMVE with BTRFS that it finally produced a sane command.
eichi_uehara 9 hours ago [-]
I beat lawyers twice before generative AI even existed.
Recently I asked Gemini a few questions about personal conflicts in everyday life. It's often too conservative, with views too shallow for the problem. So I still handle human conflicts myself. I only outsource the templated stuff like routine chat replies or marketing copy though it saves me huge amount of time. People who quote AI in serious conflicts are too weak to handle them on their own.
lp4v4n 2 hours ago [-]
Honestly it's not surprising that AI provided answers that were flagged less often as "pedagogically harmful" if we take in account that somehow LLMs create an "average" of all knowledge they ingested.
airstrike 9 hours ago [-]
Yes, LLMs are great at search. That's not news.
Aperocky 9 hours ago [-]
> rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
That's the problem, you never know when the 25% deliver a true stink bomb, and that's not considering prompting - while a fair prompt/question maybe considered objective, it's very easy to stray.
10 hours ago [-]
Esophagus4 10 hours ago [-]
Yeah this could be interesting. A lot of the spotlight has been on “law firm stuff” like demand letters and writing contracts…
But imagine if a dev team didn’t have to go engineer -> product manager -> legal team to get a question answered on local data retention requirements. You could ship that much faster.
ares623 9 hours ago [-]
Would you take responsibility for missing details about local data retention requirements?
zuzululu 9 hours ago [-]
honestly if you just avoid EU and China
you can get away with anything
jedberg 9 hours ago [-]
California too.
applfanboysbgon 9 hours ago [-]
And with those three places listed you've ruled out literally 40% of the world economy. Great, you can ship your product in bumfuck Nebraska.
Esophagus4 9 hours ago [-]
Yes.
If the only purpose of asking a lawyer is transferring risk (aka cover your ass) while getting the same advice as an LLM, that’s slowing down delivery for purely bureaucratic reasons.
I’ve seen that mentality at big companies where everyone is scared to stick their neck out and be accountable for a decision. And nothing gets done. Drives me crazy.
But the people who move up are the people who take ownership and get shit done (and are right a lot).
(BTW, I have been at companies that were sued by regulators. They never really punish the individual(s) who were in the room when the decision is made. So your worry is kind of misplaced.)
teiferer 5 hours ago [-]
Question is: if a legal question is answered incorrectly by an LLM, who is going to be held responsible?
vessenes 6 hours ago [-]
* Gemini 2.5 Pro (no outside resources), and
* NotebookLM (not versioned -- with added legal resources).
NotebookLM was considered slightly better than 2.5 Pro by the evaluators.
wilg 10 hours ago [-]
> In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
I wonder to what degree the AI was just better at communicating. My experience with attorneys is that they are often some of the worst writers.
applicative 8 hours ago [-]
The writing is always fluid and grammatically flawless. This carries much more weight with us than we believe. I know the illusion well from decades of grading college papers. Many of the highest quality students use English as a second language, and I know this, but an American well trained in writing, grammar, spelling always gives an impression of superiority. (Being well trained in writing, grammar, spelling etc is of course high merit, which is how the illusion forms - it is basically an illusion of global 'intelligence')
falcor84 10 hours ago [-]
Yeah, 75% win rate is a ~200 points Elo difference, which is quite massive.
jshier 10 hours ago [-]
I do wish they'd used some more objective criteria. Simply being preferable one of the things LLMs have trained for since the beginning, hence its sycophantic nature.
adornKey 2 hours ago [-]
Maybe sycophantic nature is a good fit for the legal system. A successful lawyer once told me that the most important thing is to know your judge. Objectivity isn't a big thing in court. They'll cite random newspaper articles as evidence and throw out expert opinions - if they like. There might be a way to appeal - but that road often is not functional.
wilg 10 hours ago [-]
What criteria would you use for judging legal arguments?
mitkebes 9 hours ago [-]
The arguments need to be based on actual law, and any cited reference cases need to be real.
There's been a lot of news stories about lawyers using AI, and then getting in trouble for citing hallucinated laws or cases. It doesn't matter if the AI response is "preferred" over the human one if it gets thrown out when put under the scrutiny of a real case.
wilg 9 hours ago [-]
Who's gonna determine that? A bunch of law professors?
voxl 9 hours ago [-]
But did they? Or did they just go off what answer felt better? Did they put in any work to actually confirm the answer? Or did the busy law professors just click through and move on with their life?
mylifeandtimes 10 hours ago [-]
maybe seeing if the case law it cited was real or imagined? Just one idea, IANAL
gamerDude 10 hours ago [-]
Well, they had the data around if the answer would be harmful to the students learning. AI was scored at 3.5% harmful answers and 12% of law professor answers were considered harmful.
king_zee 10 hours ago [-]
I think there will be a market for firms that aggressively market themselves as non-AI, and then as more people turn towards that human connection we'll go full circle
rayiner 10 hours ago [-]
Nobody wants to pay their lawyers more than they have to. There will be a huge market for firms that can use AI to avoid charging clients for $1,000/hour junior associates.
zuzululu 9 hours ago [-]
that worked out for artists and translators right ?
citizenpaul 10 hours ago [-]
If you want human connection the legal system is not where you are going to find it, period.
I don't think there will be any such market for "non ai" law. If I'm involved with the legal system I just want out as quick as possible as cheap as possible.
applfanboysbgon 10 hours ago [-]
Bad legal advice will keep you dealing with the legal system for much longer and at much greater cost. Something being cheap and quick upfront doesn't mean it will be cheap and quick by the end of the process.
Esophagus4 10 hours ago [-]
But isn’t this study saying that the legal advice could actually be better with AI?
A bit of extrapolation from the study, but not a crazy stretch.
applfanboysbgon 9 hours ago [-]
Maybe, although I would be extremely hesitant to extrapolate from this one study and trust my legal life to an LLM. One thing that's worth noting, though, is that regardless of the quality of objective legal advice in the abstract, for a lot of smaller scale stuff the human connection actually is literally what is important. There are ambiguities in the law, which are not resolved deterministically but rather at the individual discretion of judges. Your lawyer, if they're any good at their job, knows the local judges and how they're likely to rule for given circumstances, which can influence their legal advice to you specifically.
Esophagus4 9 hours ago [-]
Fair.
But I could also see a world where that, too, is fed to models for hyper-local results.
Could be a way off, but I could see it.
zuzululu 8 hours ago [-]
I think you are ignoring that there are bad lawyers and they give bad legal advice too
Even the good ones will not step above and beyond what they are paid to do
but an AI ? it will and can go above and beyond
gaiagraphia 9 hours ago [-]
Incredible that the common people will be able to wrestle the right to rule of law away from the bloated legal caste, who have built themselves quite the moat.
The inaccessibility of justice is a huge driver of inequality. Any tools which bridge this gap will help make a more just society.
homeonthemtn 10 hours ago [-]
Personally I think this is very good. One of the hardest things out there is maintaining a society in the face of changing times and it's because law is dense and slow.
I think, in the right hands, this could be huge.
wholinator2 9 hours ago [-]
It turns out everybody has at least one right hand, even the people we trust the least.
gamblor956 5 hours ago [-]
While they provided the questions that professors and LLMs were asked to respond to, they don't include any of the answers from either the humans or the LLMs, so there's no way to independently verify that the LLMs actually returned "better" answers.
Given the number of responses the professors were asked to rate (200 each), they probably graded them the same way that bar exam responses are graded: quickly and superficially. Not surprising that LLMs achieved higher scores in this scenario, since they excel at producing superficially nice answers that don't hold up under scrutiny.
Also...unless statistics has changed in the past 2 decades, the math in the charts doesn't math. That's probably why they're leaving out the actual numerical data. I also wouldn't be surprised if we learn in the coming days that the charts were AI generated.
xyzal 3 hours ago [-]
This contradicts my anecdata.
Recently, I tasked Opus 4.6 to study a new Czech building permit law in conjunction with some waste disposal regulations and the result was disappointing. The model could not stop drawing conclusions from obsolete regulations in its training dataset, even when given the fulltext of the new law. The usual "you are totally right" also applied and its conclusions were most of the time obviously wrong even to a human with cursory knowledge of the subject.
I ended with studying the relevant regulations myself over the weekend.
cess11 3 hours ago [-]
I skimmed portions of the study but didn't manage to figure out whether this actually measures a preference for confident mediocrity.
Eufrat 2 hours ago [-]
What is the point of this conclusion? That law professors like the tone and verbosity of AI slop? Okay?
Leptonmaniac 43 minutes ago [-]
I had a similar thought. What if the result, statistical and significance critique aside, mostly means that when it comes to first-year tutoring of law students, the vibe, tone and overall presentation of arguments weighs a lot, maybe even more than the factual arguments themselves?
In such a framing I don't find it surprising at all that teachers prefer the more polished answers generated by AI, because if LLMs are good at one thing, it is being confident in whatever they generate and present it convincingly.
Thaxll 9 hours ago [-]
AI will never convince a jury though.
jojobas 9 hours ago [-]
A couple of acting classes might be cheaper than a lawyer, then you can go all out representing yourself.
t0lo 9 hours ago [-]
Library outperforms student... more news at 9
lern_too_spel 6 hours ago [-]
This was an open book test. The real problem with this study is that winning the most head-to-head preference tests is not the right metric. It doesn't much matter if two answers are right, and one is written a little better than the other. It matters quite a lot if one answer is right and another is wrong.
The authors point out that this other metric was computed in prior work and incorrectly dismiss it as being not as good as winning percentage in head to head competitions. The cited prior work shows that the models fare poorly on that metric. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5166938
apparent 6 hours ago [-]
Except the library outperformed the professors, which is quite a bit more impressive.
t0lo 9 hours ago [-]
More great news from the prestigious university where 40% of students claim they are disabled
Stanford and its donors of course want to replace anyone but its administrators, so they cheer on such anti-intellectual nonsense.
signatoremo 9 hours ago [-]
This is the state of HN. Created new account. Accused without evidence. Emotional clickbait.
vessenes 6 hours ago [-]
I vibe coded hn10k earlier this year. You could choose to see pages with comments only started by 1k+, 10k+ or 100k+ karma contributors. I'm too lazy to keep it up, but I found 1k and 10k both to be better experiences than "vanilla".
rimliu 5 hours ago [-]
Yes yes, the IPO is near.
charliewang0322 4 hours ago [-]
[dead]
steele 10 hours ago [-]
[flagged]
jimbokun 10 hours ago [-]
[flagged]
jatora 9 hours ago [-]
definitely not needed if you're in the middle-man slime trades (law)
jimbokun 9 hours ago [-]
In an advanced economy everyone’s the middle man for something. We’re not self sustaining agrarian farmers anymore.
zuzululu 9 hours ago [-]
what do you think software devs do all day
jatora 6 hours ago [-]
create value and utility without purposely gatekeeping and hamstringing society
Waterluvian 9 hours ago [-]
the memes were nice tho
10 hours ago [-]
fgh_ask 10 hours ago [-]
[flagged]
maxbond 10 hours ago [-]
Just so you know, I have nothing to do with Stanford, but I am flagging this as conspiratorial nonsense. So when you comment is flagged, I just want you to know that it doesn't confirm your belief, it's just that this comment harms discussion and so must be removed.
hoppyhoppy2 10 hours ago [-]
>Don't feed egregious comments by replying; flag them instead. If you flag, please don't also comment that you did.
Yes, mea culpa. Occasionally I break that rule on my own judgement. Feel free to flag my comment. (I think it's important to disconfirm conspiracy theories.)
thin_carapace 9 hours ago [-]
for what it's worth I have no idea why it would be nonsense to question institutional motivations especially in the context of an academic article that could easily be corporate propaganda, I also think that shutting conversations down is much more harmful than discussing topics that are potentially harmful
maxbond 9 hours ago [-]
Completely unevidenced conspiracy theories can only harm the discussion. The only possible benefit is to disconfirm conspiracy theories and discourage paranoid thinking. The odds that Standford as an institution are astroturfing on HN round down to 0.
What they're almost certainly observing is that these critical comments are being flagged as inappropriate. People make inappropriate comments that happen to contain criticism all the time, and I frequently see people edit them to declare that they were flagged because the group they're criticizing is astroturfing. It's virtually never the case. I've never seen it happen.
But to be clear I am completely ambivalent on Stanford and if you want to criticize them, more power to you.
thin_carapace 8 hours ago [-]
may I ask why you effectively said 'conversation over due to harm reasons' instead of asking for evidence to support the conclusion that you believe is not possible? I don't see why it is inherently harmful to discuss the seemingly impossible. I also don't see why it's relevant to bring up your n=1 sample (although it is as relevant as my n=1 sample, which has plenty of astroturfing witnessing [unspecific to Stanford])
maxbond 8 hours ago [-]
[dead]
19skitsch 10 hours ago [-]
uh alright buddy
10 hours ago [-]
aetq51 10 hours ago [-]
[flagged]
rfw300 9 hours ago [-]
A law professor studying AI has an affiliation with the center at their university that studies applications of AI? Scandalous!
dang 9 hours ago [-]
Would you please stop creating accounts to post this?
wilg 9 hours ago [-]
You're suspicious that the person doing academic research on how AI applies to law has a job related to research on law and AI?
runarberg 9 hours ago [-]
You are not? It is at least worth investigating how much this professor benefits from AI companies. In fact this is HN. Let me come back to you in about 10 minutes.
EDIT: 10 min later. I give up. I tried to find who is funding HAI, and came empty handed, usually you can see that in their yearly reports, but no such luck for me. I know Google and Bill Gates are big donors, so take that as you will.
ares623 9 hours ago [-]
Running out of IPO juice. Each bump is less effective and lasts shorter.
bko 10 hours ago [-]
Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.
I'm getting more convinced. I mean, sure it makes dumb mistakes sometimes but its a particular set of self serving mistakes, commenting out tests in order to pass. We obv don't want this behavior but I wouldn't say it's dumb.
It'll be like the Turing test, which we just blew past years ago and no one cared. After all the hand-wringing about sentience and rights of the AI if it passes the Turing test, and now we just have AI bots running 24/7 writing slop.
How does everyone else feel?
acdha 9 hours ago [-]
> Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.
He stands to make billions if enough people believe him — unless you also do, consider that you’re the mark. For example, if that was true, it would have to mean that AI companies either aren’t letting customers use the good models or are instructing them to frequently make errors which reveal a fundamental lack of reasoning ability.
Consider also that his wealth means he hasn’t had to defend an idea stringently since the 90s. I wouldn’t be surprised if he does think LLMs give deep answers because it often looks that way until you critically review the response and ask questions like what’s missing which require you to have a decent understanding of the problem domain.
moregrist 9 hours ago [-]
Marc Andreessen has a strong financial incentive to feel this way and to convince others to feel this way.
I also think it’s easy to think that AI gives good answers if you don’t know the field well. In fields where I know the material, the answers are pretty variable and can be quite bad.
paulmist 9 hours ago [-]
Knowing the question is half of the answer. LLMs are great at scoping your context and answering precisely what you asked; it's also why they go off the rails when they misunderstand a part of your question. Incidentally, they're great at "knowing" and reaching for knowledge.
Humans have the advantage of perspective. We always lack some knowledge and answer broadly. This is bad if you have a particular goal in mind, but better if you're just generally learning, because you see more and learn to discriminate the correct from the wrong. And most importantly, being wrong is part of human ingenuity - because sometimes we turn something "obviously" wrong into something right.
scottfalconer 9 hours ago [-]
Getting the right answers is just half of it, you need to know the right questions to ask. I haven't yet seen AI crack that one.
foolserrandboy 9 hours ago [-]
He would tell you NFTs were AGIs if it might get you to buy them.
rvz 9 hours ago [-]
> Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.
Investor with vested interest in AI companies makes claim of reaching "AGI".
He is one of the last people to listen to about AGI. Unless the term "AGI" means something entirely different to him vs to independent researchers vs to CEOs, since the term has become entirely meaningless.
12AHg 9 hours ago [-]
[flagged]
futuraperdita 9 hours ago [-]
I’m not an AI stan by any means and certainly no fan of Andreessen, but using the term “clanker” immediately biases your statement and can discredit what is a well-referenced or well-meaning comment.
atleastoptimal 4 hours ago [-]
And this was done with Gemini 2.5
By the time any research study is done on AI is published the models are already 0.5-1 generation ahead. Even this bullish outcome for AI models and their ability to perform useful work does not reflect how good they are now.
himata4113 3 hours ago [-]
There is quite a simple solution for many of the problems described in the comments: Make drafting legal papers a defined interface.
If you think about it and extract sematics of any law you get something that looks familiar, sort of like code. Of course there's some complexities where certain phrases can mean different things, but legal papers in a way are written like they're programming languages already especially when it comes to law.
First we would have to define a language that can handle ambigious operations and we alread y have this with programatic proofs where n should land in x. So in the end I'd assume it would look something like this in a two party dispute:
This is very simplified and pseudo like language, writing out a full contract would be as long as a real contract.
DEFINE DEFENDANT "A Corp"
DEFINE PLAINTIFF "B Corp"
DEFINE CONTRACT CONTRACT(PLAINTIFF, DEFENDANT, 3054-41-95)
// attaching extracted requirements, definitions and obligations of contract
FACT PLAINTIFF delivered(goods) ON 7054-34-99
FACT DEFENDANT paid(0) OF CONTRACT.amount
CLAIM breach WHEN obligation(DEFENDANT, "pay") IS NOT satisfied
PROVE breach:
REQUIRE PLAINTIFF performed
REQUIRE DEFENDANT.paid < CONTRACT.amount
ASSERT delay WITHIN reasonable(time)
IF PROVE(breach):
AWARD PLAINTIFF (CONTRACT.amount - DEFENDANT.paid) + interest()
ELSE:
DISMISS
Then you would run a proof based LLM to generate it into target language and since we already had an example of this from one of the AI labs we know it works. Automatic citations and supporting proof would be automatically populated from reviewed legal -> DSL extracted papers as supporting evidence.
I am sure that many AI labs are working on something similar already and we will see something like that in the near future as proof based llms evolve.
That's the entire point, though!
The legal academy is supposed to have outlying opinions on things and present novel philosophical answers to questions. (And questions to answers!) So in addition to the statistical arguments against this paper made elsewhere, to me it doesn't real much new information.
Figure 2 (page 6) screams problems. There's only 16 professors (3k comparisons each?!?!) and the professors are all over the place. That's very high variance, suggesting the study has no meaningful statistical power. Poor instructor 16 can't catch a break lol
There's also really clear bias given that the main results only feature Google models. Other models show up elsewhere, why not there?
I'm no lawyer, but I'm a pretty competent statistician and can confidently say this paper has a smell to it. I can't call it bullshit, but there are red flags all over
> As judges, the professors then completed 2,918 blinded, forced-choice comparisons (median per judge: 200), each time indicating which of the two anonymized responses, from the instructor or the LLM, they would rather give to a student
IF the right questions are asked, and IF steered into and corrected at a few crucial points. IF not it goes off in the wrong direction really quick and that's a problem that's still mostly unsolved in the last 2 years.
And that can be catastrophic in high risk environments, like legal, medical or high risk software products where being wrong in the wrong place can mean bankruptcy or even cost a life.
I have a few marketing website where I let the CEO's run crazy with Claude cowork, they are making PR's like a madman, but they are not allowed to touch any of the API's & platforms where there is real user data & sensitive information.
With that kind of logic ... anything is possible.
The point is that if the study can't validate the claims being made then we can't actually extrapolate from that claim.
There’s also the fact that they can’t possibly keep improving frontier models at the same rate (I.e. training investment) when investment starts slowing down. The amount of cash being burned is completely unsustainable and you’re already seeing some pullback.
Every new model might not be a leap like it used to be, but give it enough time and improvements add up.
The further we get into this, the more AI feels like 3-D printing. Significantly bigger and will be more widely used for sure. But nowhere near the “new industrial revolution” that all these companies are making it out to be
But is it a surprise law professors aren't great statisticians?
If you have 100 responses from 1 professor, and the AI wins 75% of the time that is very likely a true signal that the AI is better than this prof. It would be incorrect to generalize this to all profs though.
Further, if you sample 16 profs and the AI beats 10 of them you can be fairly certain that the real percentage of profs it beats isn't 10%. Further, when estimating the probability that the AI beats a random prof, it's the relative estimation error that scales with 1/sqrt N. If you have a coin and it lands heads up 16 times, that tells you something quite robust about the coin.
Reasonably estimating confidence intervals at small N and high p is not trivial. But it can be done.
A good heuristic is "add 2 successes and 2 failures" which is due to Agresti & Couli.
See down the page here for source papers:
https://en.wikipedia.org/wiki/Binomial_proportion_confidence...
So your alternative is to not have any studies and everyone can just stump up anecdata as "evidence" for the capabilities of these models?
I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
Any lawyer who isn't using LLMs for research is behind the curve, though. They are unbelievable at finding niche cases you would never have found on your own. Previously it was a lot of exact search term matching, which is inherently useless for a lot of legal research. I need something that can search on vaguer terms, which AI can do incredibly well. Just check the results. I'm sure the LLMs from Lexis Nexis/Westlaw are probably better than the general purpose ones.
LLMs make fantastic paralegals. If you're doing any legal work, you should be using it, even if it's just to shoot ideas at. Have it play devil's advocate. My friend always has it play the other party's lawyer to see what all the counter-arguments are going to be.
Just like you would with software development. If you care about what you are creating, CHECK THE OUTPUT.
Naive question from an outsider: aren't there searchable databases of cases (with complete text) so that citations could be checked automatically, either by the same or an independent agent?
[1] https://www.legifrance.gouv.fr/
[2] https://legal.thomsonreuters.com/en/westlaw/plans-and-pricin...
The "biggest problem" being the one thing that is trivial to verify against concrete databases is a bit convenient don't you think?
I think it's more likely that it makes mistakes evenly but the one thing that you are able to check with certainty is the only place you discover the errors.
The knowledge cut off gap means the models sometimes don't know about the most recent case-law, in a given situation.
I've seent his happen multiple times now. Accountants and legal professionals advising clients based on outdated information assembled through chat-gtp, claude and copilot.
Professionals drafting letters and missing recent case-law which handles their exact case. It's unreliable.So it can save you some work; but it can't save you all of the work. And in some cases its mistakes really force you to redo all the work, and more, to be thorough and have confidence in the result.
But they can perform live websearches or go directly to a DB specified.
I liken it to me googling things as a sysadmin vs. Jane from accounting doing it. The non-tech end user is far more likely to make the problem worse, or install something sketchy from the ad riddled results than I am, or one of my help desk employees are.
I wouldn't trust myself to draft an important legal document using AI without the advice of a lawyer, much like I wouldn't really want to rely on my lawyer to use AI to write code for me.
I find those that are best and make the greatest use are the ones who remain skeptical but also use the tool. The same people who were already nuanced and picky before AI. The same people who already doubted and questioned their own work, and used that suspicion to help prevent them from having over confidence in their own work. If you weren't willing to just "lgtm" with your own code, it's difficult to do that with AI.
(To be clear, I'm not saying perfectionists. Some might call them that because the picky people have higher standards, but a good expert has to also understand that perfection doesn't exist. That's often a driving force in the suspicion! This also tends to cause them to continually improve)
The danger of those mistakes creeping in also grows exponentially the farther a lawyer strays from their core legal expertise. There are a few statutes I know inside and out, and I can spot LLM analytical errors related to them in a split second, but once I venture out into domains where I am not an expert (but where I am nevertheless reasonably qualified to practice), it becomes much harder to spot drafting mistakes because I have not refreshed my own understanding of the law by reviewing the relevant cases or statutes as I would when drafting the analysis myself from scratch.
Yet that is exactly what a lot of C-Suiters (many of whom are lawyers), are doing.
i think devs overestimate their own role and underestimate others
i am seeing lawyers and doctors roll out their own software with AI
but we dont have their training and experience
I would imagine it's similar in law, in that it takes a lawyer or judge to know where the foot guns lie.
Absolutely not harmless if you're the executor of an estate forced to deal with a screwed up AI will. I just handler my dad's estate this spring. It's a frustrating and confusing process even with the simplest of estates.
Median household net worth is in fact somewhere in the $100k-200k range, which is definitely something that could be meaningfully called an "estate." (Most of this tends to be the house, the median net equity in which is about $190k as of 2022).
Source: https://www2.census.gov/library/publications/2024/demo/p70br...
[1] This doesn't mean "homeowners," rather it's a recognition that assets for married or cohabitating couples are usually commingled.
An "estate" is a legal term for property, assets, and liabilities a person leaves behind upon their death. A family member is a top practitioner in the field of estate planning and resolution, and some of the messiest estates they have handled are pro-bono cases of exactly the type of people you would put in italicized "most people": poor, not really able to upkeep a house they inherited from a relative which hadn't had title properly transferred on a previous death because they didn't have money for an attny, now can't get a loan to fix the roof...
Yeah, if you are homeless, carless, and have only the clothes on your back and a shopping cart of stuff, you don't have an estate. Everyone in the middle class in the US has an estate. Much of the time it passes automatically to their spouse on death, but it's still an estate.
And if you are concerned about where it goes, get a GOOD attny. There are many bad ones hanging out their shingle as "Trust & Estate" attnys, and some of the next messiest cases are fixing problems made by those not-so-good attnys.
And NO, AI is not good enough.
The time lag between drafting and "deployment" also makes for much less effective, much more expensive debugging loops. You can deploy your code to prod in seconds, see an error pop up in the logs, and immediately start debugging. But it will take at a minimum days and frequently as long as several years before an error in a contract or a court filing will be detected, and often the error is beyond correction at that point. Thus, the errors are both more difficult to detect and to resolve.
And the consequences of error are often much greater, both because they are not correctable and because a legal error may risk someone's life, liberty, or substantial property. Although that's not categorically the case, obviously bugs in certain safety critical systems can be as bad or even worse than legal mistakes. But in general, most software is lower stakes than most legal writing.
On the flip side, LLMs do seem to do a better job with basic style and structure for legal documents compared to code. Things like following IRAC format, citing assertions of law (although hallucination remains an issue), and writing comprehensible sentences. These would be the equivalents in code to best practices like good comments, cohesion, consistent use of design patterns, test coverage, clear variable names, DRY, etc. Although the better performance on those more qualitative metrics may just be because even the longest legal documents are typically simpler in structure and have fewer lines of text than a large, complex codebase. Or maybe it's because LLMs are trained on natural language text more than on code. Or because natural language is more forgiving than code, in that minor variation in diction or grammar is unlikely to have any significant effect on how the document is interpreted, whereas even single character errors in code can have enormous effects.
For murder that's not such a huge deal because the statutes are typically easy to track down and don't really differ all that much substantively, but once you get really into the weeds on something like commercial contracts it can be a huge pain to do cross-jurisdictional research.
And that's just a tiny, super obvious example of how impenetrable statutory law is, which isn't even the really pernicious problem. Case law is infinitely worse. It makes me absolutely furious how difficult legal research still is. The Westlaw/LexisNexis duopoly is a moral crime and wildly destructive to the quality of government in this country. Every single written court opinion should be publicly available for free on the internet in an easily searched format. It would cost practically nothing to achieve. We're talking about less text than Wikipedia hosts. Yet still many states make it almost impossible to access case law. Even though these cases are law. Binding law that we are supposed to follow, yet we cannot even easily access. It's insane, and largely perpetuated by the complacency of lawyers who can charge others for what should be free, the lobbying of the duopoly, and the incompetence of politicians.
If all of the laws were consistently available and stored in reasonable, consistent citation formats (I would settle for hyperlinking as a replacement for the rat's nest of wildly varying jurisdiction-specific citation systems), it would even be possible to introduce a form of unit testing for legal drafting that would allow us to automatically verify if the LLM hallucinated a citation.
It also doesn't help that we (for what were at the time very good reasons) moved away from the system of legal writs that used to provide fairly standardized, almost "cut and paste" templates for legal filings. So now every legal document (filings, memos, contracts, court opinions, statutes) is drafted like a bespoke, artisanal creation with few strict structural or stylistic conventions. That makes automated interpretation much harder than it needs to be.
e.g., https://www.npr.org/2026/04/03/nx-s1-5761454/penalties-stack...
I think on the contrary, LLM providers accumulate huge logs of interaction with their users, which elicit that tacit knowledge and mine it and humans cooperate willingly in order to solve their tasks. Just imagine the corpus of sessions for scientific research, education or software development, it is probably the largest such collection ever to exist. Trillions of HITL tokens per day flow into those logs, carrying our perspectives, choices, original ideas and tacit knowledge. I call this the "human-AI experience flywheel". It's the new stackoverflow, next model generation is based on interaction data from previous one.
My favorite example of this is knowing how to untangle a big pile of cables. There are robots now which can untie a single knotted cable, but I don't think any can do a pile of cables yet. https://www.youtube.com/watch?v=vp-94rsherE
can't get more foot gun than "well according to [fiction] it is a well established practice (that the defendent is guilty)"
One thing I learned, just bite the bullet and re-write the whole fucking will instead of making riders.
Piecing the will together from riders was terrible. Al the clauses fell away everyone got older. The final will could have been 8 pretty clear pages.
The other part that is hard is just knowing all of the things that happen with assets and a passing. Luckily we had another lawyer and financial folks to advise us. It was still a lot and not that easy to find details. This was pre-ai that would have helped walk through his shit.
Such a document may not make a difference to the person that eventually will have died, but it can make or break the life of generations to come in countries that are so heavily optimized for dynasty building like the US.
I don’t know if that’ll be true for long. I just had my colleague who’s a very competent engineer IMO hand me a frontier model vibed PR to review (after reviewing it himself, he claims) which contained random variable assignments, conditionals that do nothing, etc. He’d never do such a thing before. People become too comfortable and get confirmation bias as well.
Or worse, use historical data to determine the laws of today.
TL;DR Its never a good idea and it will bite you.
1. https://finance.yahoo.com/news/valve-wins-trial-against-pate...
Reading it makes me extremely suspicious on how cherry picked this was
In the framing of using LLMs as legal tutors, with the implication of lowering the cost of legal training, this seems like a socially-positive outcome. Furthermore, it feels kind of intuitive to me that any contemporary system operating with an LLM and access to legal reference material will be prepared to answer _student-originated questions_ comprehensively and with breadcrumbs or direct references to educational/source materials, as seems to have been found in the study.
The authors explicitly and intentionally emphasize that many legal questions require contextualization, as opposed to some discrete calculated answer. The result of the study implies that the LLM-based systems were capable of using what many of us here understand to be the "stochastic best-fit algorithmic generation" of a contemporary language model to adequately contextualize a student's question, providing insight into the trade-offs or complications implicit in the question, while then, critically, _meeting the professional standards of legal educators in explaining that complexity to a student_.
Realistically, I would hope this provides some confidence to readers of HN that they can actually ask a legal question to an LLM and expect the response will explain the complexity of the law in relation to the question. This is great news, and is likely the minimal pre-work any of us should do before actually consulting a lawyer, if time permits.
On the other hand, I do _not_ think that this study provides any indication that an LLM is prepared to actually provide direct legal counsel. Possibly in the same way that a legal textbook does not replace legal counsel, or perhaps more accurately, the same way that stumbling upon a legal case study for approximately the same situation you're in doesn't guarantee you'll have the same result.
But, it makes me wonder, will clients be able to use these AI-attorney systems in the future, in the court. Where they basically either just parrot what the model is instructing them to do, or - I dunno - give the model permission to speak for them (while waiving liabilities).
I have no doubt that some complex AI system can perform better than a bottom-tier, overworked lawyer.
One wrong advice clump and, like a step onto the wrong path while hiking, all subsequent steps go in the wrong direction. And sycophancy tuning means marginal one-sides takes get presented as sure-fire things.
I’m of the opinion that the big wins aren’t in using the LLMs to do the work (legal, in this case), but rather to refine and improve the dialog and presentation from all parties. A court-centric LLM that could give likely procedural needs to a litigant, and a law-firm-centric LLM could help a pro se litigant create a meaningful and refined set of questions for lawyer consideration, condensed and targeted, saving all parties time and confusion while meeting the clients linguistic needs ‘where they are’.
All the lawyers know things LLMs never will, the law is interpreted, and the written part isn’t engineering grade facts but suggestions interpreted in context. Arguably this is a racket and a thin veneer of plausible deniability for authoritarian rule. But as the law stands even with federal statues and citations from the courts website, practicing lawyers will frequently end up explaining that in this county/country/court/jurisdiction The Way of Things is different.
This is a pretty limited introductory course based on what it says in the methods of the paper itself.
EDIT: just found out that Google is a major donor to HAI. So this research is at least partially funded by Google. Which is probably the reason the authors fail to declare no conflict of interest.
My understanding is that Civil Law (most of the world excluding UK, US, AU) is like a program: you feed it a situation, it outputs a decision, every once in a while you edit it.
Common Law (UK, US) isn't really a program, but you could stretch and say it's a state machine that has been running since the country started. Every interaction sets a new precedent and changes the state. But the programming analogy falls apart because no one in the right mind would design such a program.
LLMs might actually be the best example of such a program though: Common Law is basically one long chat with an LLM, hundreds of years long.
Before LLMs came along, a Common Law system seemed to have a finite time limit before it's co-opted by wealthy people with the resources to read the whole history. Now I think maybe can push it a bit further.
But it's still a terrible program.
There are however LLM context building techniques that anchor completions in data structures that persist the structure of claims that support the conclusion contained in a completion. Lots of different patterns exist —organizing logic in language is a rich domain— but the one I’ve liked the most is something called a Claim Dependency Graph that models the relationships between atomic claims as graph edges.
There’s a whole suite of operations you can perform on these structures, and “reconstruct how you came to this conclusion” is absolutely one of them.
Model interpretability work has advanced a lot. Arguably we already can explain AI decision-making better than human brains.
The point is familiar but there are good illustrations in the Atlantic article by a book editor. At first it seems abstract AI hate, but then she gets to the details. AI text cannot be edited. https://www.theatlantic.com/technology/2026/05/how-to-tell-a... or https://archive.ph/YJsGK
Asking the LLM in a way where it annotates its sources, it can greatly increase the pattern matching to closely simulate logic, just like in humans.
I understand the question of why did you say this, not that, I have seen other ways of asking that which do not seem to trigger the LLMs over-response in the other direction.
The quality of LLMs depends heavily on, among other things, how you word your questions.
Knowing the correct questions to ask is not something most students know how to do given that it tends to require a fair bit of pre-existing domain knowledge.
Julian Nyarko
LOL!If a person using the service is given inaccurate legal advice and acts on that advice, the person can't be charged with a crime, can't be given any civil penalties, etc., as long as the law in question is non-obvious.
Obviously if by some exploit, some fundamentally obvious crime (murder, theft, obvious fraud, etc.) is said to be legal, that wouldn't apply, but of course the service should try to prevent those kinds of exploits anyway.
Could limit this to something like business regulations to begin with, or even specifically for small businesses, or contracts within some time limit and dollar amount that would otherwise be coverable by small claims court, etc.
Attorneys will be using LLMs for convenience but they will not disappear, because there needs to be an ultimately human responsible of the decisions.
THEN I find a human lawyer and give AI's answers to them and say "Can you find any errors in this? Can you improve it?" .
That way I think my legal bills should be smaller because the AI has already done most of the work. What do you think? Which LLM is best for legal work?
Please see attached contract we received from [counterparty]. ChatGPT says blah, blah and blah should be revised. What do you think? Is there anything else that we should change?
It's bit like with doctors, you'll want a second opinion, if you can afford it.
Probably for important deals, detailed human review will be expected.
Maybe the real value-add will be the insertion of language that LLMs won't be able to figure out, but which will be favorable for the side that inserted them.
i do second phase on codex, by asking to download all pdfs and extract all text of laws it references. can repeat fully local research step.
after i ask gemini to find issues and criticize.
UPDATE: there many legal skills on github to try, not used so any yet
I killed my Arch installation and was stuck at the GRUB prompt.Unwilling to brush up my rusty knowledge of GRUB syntax, I asked Gemini for help. The commands Gemini suggested would have wiped my hd...
Once Gemini was told that I was using BTRFS, the suggestion from Gemini looked a bit more sane, but still looked incorrect to me.
It was only after I informed Gemini that I was using a NMVE with BTRFS that it finally produced a sane command.
That's the problem, you never know when the 25% deliver a true stink bomb, and that's not considering prompting - while a fair prompt/question maybe considered objective, it's very easy to stray.
But imagine if a dev team didn’t have to go engineer -> product manager -> legal team to get a question answered on local data retention requirements. You could ship that much faster.
you can get away with anything
If the only purpose of asking a lawyer is transferring risk (aka cover your ass) while getting the same advice as an LLM, that’s slowing down delivery for purely bureaucratic reasons.
I’ve seen that mentality at big companies where everyone is scared to stick their neck out and be accountable for a decision. And nothing gets done. Drives me crazy.
But the people who move up are the people who take ownership and get shit done (and are right a lot).
(BTW, I have been at companies that were sued by regulators. They never really punish the individual(s) who were in the room when the decision is made. So your worry is kind of misplaced.)
NotebookLM was considered slightly better than 2.5 Pro by the evaluators.
75% win rate seems pretty good!
Paper link: https://law.stanford.edu/wp-content/uploads/2026/06/salinas_...
There's been a lot of news stories about lawyers using AI, and then getting in trouble for citing hallucinated laws or cases. It doesn't matter if the AI response is "preferred" over the human one if it gets thrown out when put under the scrutiny of a real case.
I don't think there will be any such market for "non ai" law. If I'm involved with the legal system I just want out as quick as possible as cheap as possible.
A bit of extrapolation from the study, but not a crazy stretch.
But I could also see a world where that, too, is fed to models for hyper-local results.
Could be a way off, but I could see it.
Even the good ones will not step above and beyond what they are paid to do
but an AI ? it will and can go above and beyond
The inaccessibility of justice is a huge driver of inequality. Any tools which bridge this gap will help make a more just society.
I think, in the right hands, this could be huge.
Given the number of responses the professors were asked to rate (200 each), they probably graded them the same way that bar exam responses are graded: quickly and superficially. Not surprising that LLMs achieved higher scores in this scenario, since they excel at producing superficially nice answers that don't hold up under scrutiny.
Also...unless statistics has changed in the past 2 decades, the math in the charts doesn't math. That's probably why they're leaving out the actual numerical data. I also wouldn't be surprised if we learn in the coming days that the charts were AI generated.
Recently, I tasked Opus 4.6 to study a new Czech building permit law in conjunction with some waste disposal regulations and the result was disappointing. The model could not stop drawing conclusions from obsolete regulations in its training dataset, even when given the fulltext of the new law. The usual "you are totally right" also applied and its conclusions were most of the time obviously wrong even to a human with cursory knowledge of the subject.
I ended with studying the relevant regulations myself over the weekend.
In such a framing I don't find it surprising at all that teachers prefer the more polished answers generated by AI, because if LLMs are good at one thing, it is being confident in whatever they generate and present it convincingly.
The authors point out that this other metric was computed in prior work and incorrectly dismiss it as being not as good as winning percentage in head to head competitions. The cited prior work shows that the models fare poorly on that metric. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5166938
https://fortune.com/article/rise-in-elite-students-seeking-a...
and where they wanted to ban words such as "chief", "stupid", "karen" and "American"
https://reason.com/2022/12/21/stanford-elimination-harmful-l...
https://juliannyarko.com/
Stanford and its donors of course want to replace anyone but its administrators, so they cheer on such anti-intellectual nonsense.
https://news.ycombinator.com/newsguidelines.html
What they're almost certainly observing is that these critical comments are being flagged as inappropriate. People make inappropriate comments that happen to contain criticism all the time, and I frequently see people edit them to declare that they were flagged because the group they're criticizing is astroturfing. It's virtually never the case. I've never seen it happen.
But to be clear I am completely ambivalent on Stanford and if you want to criticize them, more power to you.
EDIT: 10 min later. I give up. I tried to find who is funding HAI, and came empty handed, usually you can see that in their yearly reports, but no such luck for me. I know Google and Bill Gates are big donors, so take that as you will.
I'm getting more convinced. I mean, sure it makes dumb mistakes sometimes but its a particular set of self serving mistakes, commenting out tests in order to pass. We obv don't want this behavior but I wouldn't say it's dumb.
It'll be like the Turing test, which we just blew past years ago and no one cared. After all the hand-wringing about sentience and rights of the AI if it passes the Turing test, and now we just have AI bots running 24/7 writing slop.
How does everyone else feel?
He stands to make billions if enough people believe him — unless you also do, consider that you’re the mark. For example, if that was true, it would have to mean that AI companies either aren’t letting customers use the good models or are instructing them to frequently make errors which reveal a fundamental lack of reasoning ability.
Consider also that his wealth means he hasn’t had to defend an idea stringently since the 90s. I wouldn’t be surprised if he does think LLMs give deep answers because it often looks that way until you critically review the response and ask questions like what’s missing which require you to have a decent understanding of the problem domain.
I also think it’s easy to think that AI gives good answers if you don’t know the field well. In fields where I know the material, the answers are pretty variable and can be quite bad.
Humans have the advantage of perspective. We always lack some knowledge and answer broadly. This is bad if you have a particular goal in mind, but better if you're just generally learning, because you see more and learn to discriminate the correct from the wrong. And most importantly, being wrong is part of human ingenuity - because sometimes we turn something "obviously" wrong into something right.
Investor with vested interest in AI companies makes claim of reaching "AGI".
He is one of the last people to listen to about AGI. Unless the term "AGI" means something entirely different to him vs to independent researchers vs to CEOs, since the term has become entirely meaningless.
By the time any research study is done on AI is published the models are already 0.5-1 generation ahead. Even this bullish outcome for AI models and their ability to perform useful work does not reflect how good they are now.
If you think about it and extract sematics of any law you get something that looks familiar, sort of like code. Of course there's some complexities where certain phrases can mean different things, but legal papers in a way are written like they're programming languages already especially when it comes to law.
First we would have to define a language that can handle ambigious operations and we alread y have this with programatic proofs where n should land in x. So in the end I'd assume it would look something like this in a two party dispute:
This is very simplified and pseudo like language, writing out a full contract would be as long as a real contract.
Then you would run a proof based LLM to generate it into target language and since we already had an example of this from one of the AI labs we know it works. Automatic citations and supporting proof would be automatically populated from reviewed legal -> DSL extracted papers as supporting evidence.I am sure that many AI labs are working on something similar already and we will see something like that in the near future as proof based llms evolve.