alt Hacker NewsThis is my experience with how LLMs "draft" legal arguments: at first glance, it's plausible — but may be, and often is, invalid, unsound, and/or ill-advised.
The catch is that many judges lack the time, energy, or willingness to not only read the documents in detail, but also roll up their sleeves and dig into the arguments and cited authorities. (Some lack the skills, but those are extreme cases.) So the plausible argument (improperly and unfortunately) carries the day.
LLM use in litigation drafting is thus akin to insurgent/guerilla warfare: it take little time, energy, or thinking to create, yet orders of magnitude more to analyze and refute. (It's a species of Brandolini's Law / The Bullshit Asymmetry Principle.) Thus justice suffers.
I imagine that this is analogous to the cognitive, technical, and "sub-optimal code" debt that LLM-produced code is generating and foisting upon future developers who will have to unravel it.
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> LLM use in litigation drafting is thus akin to insurgent/guerilla warfare: it take little time, energy, or thinking to create, yet orders of magnitude more to analyze and refute.
The same goes for coding. I have coworkers who use it to generate entire PRs. They can crank out two thousand lines of code that includes tests "proving" that it works, but may or may not actually be nonsense, in minutes. And then some poor bastard like me has to spend half a day reviewing it.
When code is written by a human that I know and trust, I can assume that they at least made reasonable, if not always correct, decisions. I can't assume that with AI, so I have to scrutinize every single line. And when it inevitably turns out that the AI has come up with some ass-backwards architecture, the burden is on me to understand it and explain why it's wrong and how to fix it to the "developer" who hasn't bothered to even read his own PR.
I'm seriously considering proposing that if you use AI to generate a PR at my company, the story points get credited to the reviewer.
"Reasoning" needs to go back to the drawing board.
Reasonable tasks need to be converted into formal logic, calculated and computed like a standard evaluation, and then translated back into english or language of choice.
LLMs are being used to think when really they should be the interpret and render steps with something more deterministic in the middle.
Translate -> Reason -> Store to Database. Rinse Repeat. Now the context can call from the database of facts.
>" justice suffers"
Possible. It also suffers when majority simply can not afford proper representation
> This is my experience with how LLMs "draft" legal arguments: at first glance, it's plausible — but may be, and often is, invalid, unsound, and/or ill-advised.
Correct, and this of course extends past just laws, into the whole scope of rules and regulations described in human languages. It will by its nature imply things that aren't explicitly stated nor can be derived with certainty, just because they're very plausible. And those implications can be wrong.
Now I've had decent success with having LLMs then review these LLM-generated texts to flag such occurences where things aren't directly supported by the source material. But human review is still necessary.
The cases I've been dealing with are also based on relatively small sets of regulations compared the scope of the law involved with many legal cases. So I imagine that in the domain you're working on, much more needs flagging.