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If I ask an LLM a very complex and specific question 500 times, if it just doesn't know the facts you'll still get the wrong answer 500 times. That's understan
by Drakim 3y ago
If I ask an LLM a very complex and specific question 500 times, if it just doesn't know the facts you'll still get the wrong answer 500 times.
That's understandable. The real problem is when the AI lies/hallucinates another answer with confidence instead of saying "I don't know".
- deleted 3y ago[deleted]
- helsinkiandrew 3y ago> If I ask an LLM a very complex and specific question 500 times, if it just doesn't know the facts you'll still get the wrong answer 500 times. Think the commenter meant use another model/LLM which could give a different answer, then let them vote on the result. Like "old fashioned AI" did with ensemble learning.
- simion314 3y agoThe problem is asking for facts, LLM are not a database so they know stuff but it is compressed so expect wrong facts, wrong names, dates, wrong anything. We will need an LLM as a front end then it will generate a query to fetch the facts from the internet or a database , then maybe format the facts for your consumption.
- ch_sm 3y agoThat‘s exactly it. It‘s ok for LLMs to not know everything, because they _should_ have a means to look up information. What are some projects where this obvious approach is implemented/tried?
- Jensson 3y agoBut then you need an LLM that can separate between grammar and facts. Current LLMs doesn't know the difference, that is the main source to these issues, these models treat facts like grammar and that worked well enough to excite people but probably wont get us to a good state.
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- samus 3y agoThis is called Retrieval Augmented Generation (RAG). The LLM driver recognizes a query, it gets send to a vector database or to an external system (could be another LLM...) and the answer is placed in the context. It's a common strategy to work around their limited context length, but it tends to be brittle. Look for survey papers.
- m348e912 3y agoThe weird problem is with LLM hallucinations is that it usually will acknowledge its mistake and correct itself if you call it out. My question is why can't LLMs included a sub-routine to check itself before answering. Simply asking itself something like "this answer may not be correct, are you sure you're right?"
- asimovfan 3y agoIts because theres no entity that is actually acknowledging anything. Its generating an answer to your prompt. You can gaslight it into anything being wrong or correct.
- ZitchDog 3y agoThe problem is that if you call it out, it will frequently change its answer, even if it was correct. LLMs currently lack chutzpa.
- Jensson 3y agoThat is a common bullshitting strategy, talk a lot of bullshit, and then backtrack and acknowledge you were wrong when people push back. That way they will think you know way more than you do. Many people will see thought that, but most will just think you are a humble expert who can acknowledge when you are wrong instead of you always acknowledging you are wrong even when you aren't. People have a really hard time catching such bullshitting from humans, which is why free form interviews doesn't work.
- Shrezzing 3y ago