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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

#121

Post by m4houk »

b800h wrote: post #86
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.

I've been saying for ages that I want to be able to fork conversations so I can experiment with the direction an exchange takes without irrevocably poisoning a promising well. I can't do this with ChatGPT, is anyone aware of a provider that offers this as a feature?

I once built something like this for fun as a side project.

You can highlight some text in a chat and fork the chat to talk about that text selection, so the LLM has context of that along with the previous chat history and it responds in a new chat (entire chat history up to that point from the parent chat gets copied over - basically inspired by the Unix `fork`).

Your text selection from the parent chat would get turned into a hyperlink to the new child chat so you can always get to it again if you're reading the parent chat.

Re: LLMs get lost in multi-turn conversation

#122

Post by tralarpa »

olalonde wrote: post #32
Helmut10001 wrote: post #14

Earlier quoted context omitted.

My experiences somewhat confirm these observations, but I also had one that was different. Two weeks of debugging IPSEC issues with Gemini. Initially, I imported all the IPSEC documentation from OPNsense and pfSense into Gemini and informed it of the general context in which I was operating (in reference to 'keeping your context clean'). Then I added my initial settings for both sides (sensitive information redacted!…

Recently, Gemini helped me fix a bug in a PPP driver (Zephyr OS) without prior knowledge of PPP or even driver development really. I would copy-paste logs of raw PPP frames in HEX and it would just decode everything and explain the meaning of each bytes. In about an hour, I knew enough about PPP to fix the bug and submit a patch. https://g.co/gemini/share/7edf8fa373fe

Interesting that it works for you. I tried several times something similar with frames from a 5G network and it mixed fields from 4G and 5G in its answers (or even from non-cellular network protocols because they had similar features as the 5G protocol I was looking at). Occasionally, the explanation was completely invented or based on discussions of planned features for future versions.

I have really learned to mistrust and double check every single line those systems produce. Same for writing code. Everything they produce looks nice and reasonable on the surface but when you dig deaper it falls apart unless it's something very very basic.

Re: LLMs get lost in multi-turn conversation

#123

Post by TheOtherHobbes »

kfarr wrote: post #109
djmips wrote: post #42

Earlier quoted context omitted.

Happens with people too if you think about it.

Who gets lost in multi-turn conversations?

Everyone?

How often in meetings does everyone maintain a running context of the entire conversation, instead of responding to the last thing that was said with a comment that has an outstanding chance of being forgotten as soon as the next person starts speaking?

Re: LLMs get lost in multi-turn conversation

#124

Post by bambax »

b800h wrote: post #86
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.

I've been saying for ages that I want to be able to fork conversations so I can experiment with the direction an exchange takes without irrevocably poisoning a promising well. I can't do this with ChatGPT, is anyone aware of a provider that offers this as a feature?

On Openrouter you can delete previous answers (and questions) and maintain a separate conversation with different models.

But it would indeed be nice to either disable answers (without deleting them) or forking a conversation. It wouldn't be hard to implement; I wonder if there's a market for just this?

Re: LLMs get lost in multi-turn conversation

#125

Post by TheOtherHobbes »

silisili wrote: post #79
coderatlarge wrote: post #24

i’ve see deepseek-coder local get into an infinite loop generating the same line over and over. which i assume without evidence is some sort of feedback from the generated line back into the generation process. so kind of getting lost in thought and going off topic from the simple .h api that my prompt asked for.

Yes! Deepseek does this to me all the time. I had 20 something files I wanted it to check and change something. The first 5 or so it did, then the sixth it rightly said everything is correct moving on. It said that for the rest of the 20, the same text over and over. I checked, and file 6 was the only correct one. It like, learned to just repeat itself after that and did nothing.

Claude does this too. It gets into a death spiral where it repeats the entire previous output instead of changing parts and moving on.

Re: LLMs get lost in multi-turn conversation

#126

Post by lewdwig »

b800h wrote: post #86
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.

I've been saying for ages that I want to be able to fork conversations so I can experiment with the direction an exchange takes without irrevocably poisoning a promising well. I can't do this with ChatGPT, is anyone aware of a provider that offers this as a feature?

T3.chat supports convo forking and in my experience works really well.

The fundamental issue is that LLMs do not currently have real long term memory, and until they do, this is about the best we can do.

Re: LLMs get lost in multi-turn conversation

#127

Post by layer8 »

Garlef wrote: post #111
stuffoverflow wrote: post #95

Earlier quoted context omitted.

Google AI studio, ChatGPT and Claude all support this. Google AI studio is the only one that let's you branch to a separate chat though. For ChatGPT and claude you just edit the message you want to branch from.

Support: Yes. But the UX is not optimized for this. Imagine trying to find a specific output/input that was good in the conversation tree.

Yes, it would be nice if you could at least bookmark a particular branch.

Re: LLMs get lost in multi-turn conversation

#128

Post by JohnKemeny »

badmonster wrote: post #120

Why do LLMs struggle so much with recovering from early wrong turns in multi-turn conversations — even when all prior context is available and tokenized? Is it due to the model's training distribution (mostly single-shot completions), the way context windows are encoded, or an architectural bottleneck? Feels like there's no dynamic internal state that evolves over the conversation — only a repeated re-parsing of stat…

We shouldn’t anthropomorphize LLMs—they don’t “struggle.” A better framing is: why is the most likely next token, given the prior context, one that reinforces the earlier wrong turn?

Re: LLMs get lost in multi-turn conversation

#129

Post by podgorniy »

There is a noticable issue when one builds LLMs interfaces around single turn conversations. Majority people expect linear conversations.

I've built telegram bot http://t.me/experai_bot as univresal UI to LLMs (with somewhat reduced functionality) exactly around idea "non-reply message means new conversation". Wanna keep context? Keep replying to replies of bot. Non-power user strugge with this idea.

--

Also I observed that OpenAI models performed worse replying to the same questions (for example list of options in reply got shorter) even with smallest system message. That was the case with 3.5, 4o. Don't know how modern ones behave. That made me decide not to include any system messages by default Still I give option to add ones if you need. You can even toggle them to mix-and-match.

Re: LLMs get lost in multi-turn conversation

#130

Post by amelius »

veunes wrote: post #96
Sharlin wrote: post #7

Seems 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 ce…

Real programmers spend a ton of time just figuring out what people actually want. LLMs still treat guessing as a feature

This cartoon needs an update for what an LLM came up with:

https://www.reddit.com/r/comics/comments/1l5tbc/update_to_th...

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