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LLMs get lost in multi-turn conversation

arxiv.org

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Re: LLMs get lost in multi-turn conversation

#151

Post by freehorse »

Benjammer wrote: post #2

It'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.

Which is why I really like zed's chat UX experience: being able to edit the full prior conversation like a text file, I can go back and clean it up, do small adjustments, delete turns etc and then continue the discussion with a cleaner and more relevant context.

I have made zed one of my main llm chat interfaces even for non-programming tasks, because being able to do that is great.

Re: LLMs get lost in multi-turn conversation

#152

Post by overflow897 »

I believe we're already using llms to evaluate llm output for training, I wonder if there's some variation of that which could be used to identify when one llm gets "stuck".

I guess chain of thought in theory should do that but having variations on prompt and context might behave differently?

Re: LLMs get lost in multi-turn conversation

#153

Post by rrr_oh_man »

voidspark wrote: post #143
bandrami wrote: post #140

Earlier quoted context omitted.

Close. Higher engagement means the user is more invested and values the solution more. The users are being engineered more than the models are, and this isn't the only example.

Are you employed at Google or OpenAI? Are you working on these frontier models? In the case of medical questions it needs to know further details to provide a relevant diagnosis. That is how it was trained. In other cases you can observe its reasoning process to see why it would decide to request further details. I have never seen an LLM just ask questions for the sake of asking. It is always relevant in the context.…

typical patterns to look out for:

- "Should I now give you the complete [result], fulfilling [all your demands]?"

- "Just say [go] and I will do it"

- "Do you want either [A, B, or C]"

- "In [5-15] minutes I will give you the complete result"

...

Re: LLMs get lost in multi-turn conversation

#154

Post by voidspark »

rrr_oh_man wrote: post #153
voidspark wrote: post #143

Earlier quoted context omitted.

Are you employed at Google or OpenAI? Are you working on these frontier models? In the case of medical questions it needs to know further details to provide a relevant diagnosis. That is how it was trained. In other cases you can observe its reasoning process to see why it would decide to request further details. I have never seen an LLM just ask questions for the sake of asking. It is always relevant in the context.…

typical patterns to look out for: - "Should I now give you the complete [result], fulfilling [all your demands]?" - "Just say [go] and I will do it" - "Do you want either [A, B, or C]" - "In [5-15] minutes I will give you the complete result" ...

> "Do you want either [A, B, or C]"

That's an example of what I'm talking about. Watch the reasoning process produce multiple options. That's what it is trained to do. That is problem solving, not "engagement". It requires more compute, not less. You see that more with the expensive models.

> "In [5-15] minutes I will give you the complete result"

I haven't seen that before and I don't see how it's relevant.

Re: LLMs get lost in multi-turn conversation

#155

Post by [deleted] »

Sharlin wrote: post #20
Terr_ wrote: post #17

Earlier quoted context omitted.

> inability to self-reflect IMO the One Weird Trick for LLMs is recognizing that there's no real entity, and that users are being tricked into a suspended-disbelief story. In most cases cases you're contributing text-lines for a User-character in a movie-script document, and the LLM algorithm is periodically triggered to autocomplete incomplete lines for a Chatbot character. You can have an interview with a vampire D…

This is a tired semantic argument that does not bring any insight into the discussion. A token-predictor could still be trained to predict the tokens “I’m not sure what you mean because of points x, y, and z; could you elaborate?”

[deleted]

Re: LLMs get lost in multi-turn conversation

#156

Post by thegeomaster »

airylizard wrote: post #49

Why I came up with TSCE(Two-Step Contextual Enrichment). +30pp uplift when using GPT-35-turbo on a mix of 300 tasks. Free open framework, check the repo try it yourself https://github.com/AutomationOptimization/tsce_demo I tested this another 300 times with gpt-4.1 to remove those obtrusive "em-dashes" everyone hates. Tested a single-pass baseline vs TSCE, same exact instructions and prompt "Remove the em-dashes from…

I slightly tweaked your baseline em dash example and got 100% success rate with GPT-4.1 without any additional calls, token spend, or technobabble.

System prompt: "Remove every em-dash (—) from the following text while leaving other characters unchanged.\n\nReturn only the cleaned text."

User prompt:

Temperature: 0.0

Re: LLMs get lost in multi-turn conversation

#158

Post by jsnider3 »

root_axis wrote: post #67
Sharlin wrote: post #20

Earlier quoted context omitted.

This is a tired semantic argument that does not bring any insight into the discussion. A token-predictor could still be trained to predict the tokens “I’m not sure what you mean because of points x, y, and z; could you elaborate?”

It could be trained to say that, but it's not exactly clear how you would reinforce the absence of certain training data in order to emit that response accurately, rather than just based on embedding proximity.

Seems easy. Have a set of vague requests and train it to ask for clarification instead of guessing.

Re: LLMs get lost in multi-turn conversation

#159

Post by tmountain »

I often ask the LLM for a concise summary of the discussion so far—formatted as a prompt. I then edit it appropriately and use it to start a new conversation without the baggage. I have found this to be a very effective technique, but I imagine it will be automated sometime soon.

Re: LLMs get lost in multi-turn conversation

#160

Post by johnisgood »

sixtyj wrote: post #133
Benjammer wrote: post #25

Earlier quoted context omitted.

That's some impressive prompt engineering skills to keep it on track for that long, nice work! I'll have to try out some longer-form chats with Gemini and see what I get. I totally agree that LLMs are great at compressing information; I've set up the docs feature in Cursor to index several entire large documentation websites for major libraries and it's able to distill relevant information very quickly.

In Gemini, it is really good to have large window with 1M tokens. However, around 100,000 it starts to make mistakes and refactor its own code. Sometimes it is good to start new chat or switch to Claude. And it really helps to be very precise with wording of specification what you want to achieve. Or repeat it sometimes with some added request lines. GIGO in reality :)

Oh my, I hate it when it rewrites >1k LOC. I have to instruct it to "modify only ..., do not touch the rest" and so forth, but GPT does not listen to this often, Claude does. I dunno about Gemini.
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