Answering Legal Questions with LLMs

This has been tried already, and it hasn't worked out well so far for NYC [0]. RAG can helps avoid complete hallucinations but it can't eliminate them altogether, and as others have noted the failure mode for LLMs when they're wrong is that they're confidently wrong. You can't distinguish between confident-and-accurate bot legal advice and confident-but-wrong bot legal advice, so a savvy user would just avoid the bot legal advice at all.

[0] https://arstechnica.com/ai/2024/03/nycs-government-chatbot-i...

Very easily done. Is that it?

> lack of common sense, false conclusions

The AI tool doesn't replace the judge/DA/etc. it's just a very useful tool for them to use. Checkout the "RAG-based learning" section of this app I built (https://github.com/bennyschmidt/ragdoll-studio) there's a video that shows how you can effectively load new knowledge into it (I use LlamaIndex for RAG). For example, past cases that set legal precedents, and other information you want to be considered. It creates a database of the files you load in, so it's not making those assumptions like an LLM without RAG would. I think a human would be more error-prone than an LLM with vector DB of specific data + querying engine.

> I don't see the utility

Then you are not paying attention or haven't used LLMs that much. Maybe you're unfamiliar with the kind of work it's good at.

> actual work involved in practicing law

This is what it's best at, and what people are already using RAG for: Reading patient medical docs, technical documentation, etc. this is precisely what humans are bad at and will offload to technology.

> actual research is required

You have not tried RAG.

> LLMs struggle to produce useful outputs

You have not tried RAG.

> LLMs are unlikely to identify issues

You have not tried RAG.

> the LLM by definition is creative analysis

You have not tried RAG.

You can load an entire product catalog into LlamaIndex and the LLM will have perfect knowledge of pricing, inventory, etc. This specific domain knowledge of inventory allows you to have the accurate, transactional conversations that a regular LLM isn't designed for.

No, connecting the facts and rules will not give you the answer.

Lawyers are only required when there are real legal issues: boundary cases, procedural defenses, countervailing leverage...

But sometimes legal heroes like Witkins drag through all the cases and statutes, identifying potential issues and condensing them in summaries. New lawyers use these as a starting-point for their investigations.

So a Law LLM first needs to be trained on Witkins to understand the language of issues, as well as the applicable law.

Then somehow the facts need to be loaded in a form recognizable as such (somewhat like a doctor translating "dizziness" to "postural hypotension" with some queries). That would be an interesting LLM application in its own right.

Putting those together in a domain-specific way would be a great business: target California Divorce, Texas product-liability tort, etc.

Law firms changed from pipes to pyramids in the 1980's as firms expanded their use of associates (and started the whole competition-to-partnership). This could replace associates, but then you'd lose the competitiveness that disciplines associates (and reduce buyers available for the partnership). Also, corporate clients nurture associates as potential replacements and redundant information sources, as a way of managing their dependence on external law firms. For LLM's to have a sizable impact on law, you'd need to sort out the transaction cost economics features of law firms, both internally and externally.

When we accept that AI is not replacing knowledge workers, the conversation changes to a more digestible and debatable one: Are LLMs useful tools for experts? And I think the answer will be a resounding: Duh