LLMs get lost in multi-turn conversation
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Re: LLMs get lost in multi-turn conversation
#2It's nice to see a paper that confirms what anyone who has practiced using LLM tools already knows very well, heuristically. Keeping your context clean matters, "conversations" are only a construct of product interfaces, they hurt the quality of responses from the LLM itself, and once your context is "poisoned" it will not recover, you need to start fresh with a new chat.
Re: LLMs get lost in multi-turn conversation
#3Humans also often get lost in multi-turn conversation.
I have experienced that in person many, many times. Jumps in context that seem easy for one person to follow, but very hard for others.
So, assuming the paper is legit (arxiv, you never know...), its more like something that could be improved than a difference from human beings.
Re: LLMs get lost in multi-turn conversation
#4My take: multi turn evals are hard because to do it really correctly you have to simulate a user. This is not yet modeled well enough for multi turn to work as well as it could.
Re: LLMs get lost in multi-turn conversation
#5[deleted]
Re: LLMs get lost in multi-turn conversation
#6It's nice to see a paper that confirms what anyone who has practiced using LLM tools already knows very well, heuristically. Keeping your context clean matters, "conversations" are only a construct of product interfaces, they hurt the quality of responses from the LLM itself, and once your context is "poisoned" it will not recover, you need to start fresh with a new chat.
Yep. I regretted leaving on memory as it is poisoned my conversations with irrelevant junk.
Re: LLMs get lost in multi-turn conversation
#7Seems like this is an aspect of their well-known overconfidence and the inability to self-reflect and recognize they have to ask for more details because their priors are too low. If you look at the output of reasoning models, it’s clear that the idea of asking for clarification very rarely occurs to them – when they’re confused, it’s just endless speculation of what the user might have meant.
This, of course, has certain implications as to the wisdom of the idea of “replacing human programmers”, given that one of the hard parts of the trade is trying to turn vague and often confused ideas into precise specifications by interacting with the shareholders.
Re: LLMs get lost in multi-turn conversation
#8[dead]
Re: LLMs get lost in multi-turn conversation
#9It's nice to see a paper that confirms what anyone who has practiced using LLM tools already knows very well, heuristically. Keeping your context clean matters, "conversations" are only a construct of product interfaces, they hurt the quality of responses from the LLM itself, and once your context is "poisoned" it will not recover, you need to start fresh with a new chat.
This matches my experience exactly. "poisoned" is a great way to put it. I find once something has gone wrong all subsequent responses are bad. This is why I am iffy on ChatGPT's memory features. I don't notice it causing any huge problems but I don't love how it pollutes my context in ways I don't fully understand.
Re: LLMs get lost in multi-turn conversation
#10Humans also often get lost in multi-turn conversation. I have experienced that in person many, many times. Jumps in context that seem easy for one person to follow, but very hard for others. So, assuming the paper is legit (arxiv, you never know...), its more like something that could be improved than a difference from human beings.
Subjectively the "getting lost" feels totally different than human conversations. Once there is something bad in the context it seems almost impossible to get back on track. All subsequent responses become get a lot worse and it starts contradicting itself. It is possible that with more training this problem can be improved, but what is interesting to me isn't it's worse than humans in this way but that this sort of difficulty scales differently than it does in humans. I would love to get some more objective descriptions of these subjective notions.