Maybe giving equal training weight to r/sovereigncitizen and r/asklegal wasn't the best idea.
this post was submitted on 30 Jan 2024
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I think the most interesting finding in this study is the following:
The models also suffered from “contra-factual bias": They were likely to believe a false premise embedded in a user’s question, acting in a “sycophantic” way to reinforce the user’s mistake.
Which when you think about how language models work, makes a lot of sense. It's drawing upon trained data sets that match the question being asked. It's easy to lead it to respond a certain way, because people who talk pro/con certain issues will often use specific kinds of language (such as dog whistles in political issues).
It might also be a side effect of being trained to "chat" with people. There's a lot of work that goes into getting it to talk amicably with people.
I had a colleague perform a similar experiment on ChatGPT 3. He's ecoanxious and was noticing how the model was getting gloomier and gloomier in accordance with him, so he tried something. Basically he asked something like "Why is (overpopulated specie) going instinct in (location)?" The model went on to list existential threats to a specie that is everything but endangered. Basically it naively gobbled the loaded question.
I had a realization recently. These things are like the reverse of the mythical Cassandra: no one can ever be sure that their information is correct, but everyone trusts what they say.
Legal questions are very case sensitive, no pun intended. It's like asking an extremely specific programming implementation question. LLMs don't do very well with those types of prompts because the narrower the focus, the less of its training data applies to it and the more likely it'll just straight up hallucinate. And they don't yet have the nuance necessary to determine that an area of case law may not be settled and is in a legal grey area.
I tried to use GPT4 in my work but it continuously gave factually wrong answers.