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.

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.

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.

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.

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.

TL;DR Its never a good idea and it will bite you.

1. https://finance.yahoo.com/news/valve-wins-trial-against-pate...

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.

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.

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.

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?

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 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.

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

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.