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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

#201

Post by [deleted] »

thegeomaster wrote: post #156
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

[deleted]

Re: LLMs get lost in multi-turn conversation

#202

Post by airylizard »

arnaudsm wrote: post #176
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…

That's a lot of kilo-watt-hours wasted for a find and replace operation. Have you heard of text.replace("—", "-") ?

The test isn't for how well an LLM can find or replace a string. It's for how well it can carry out given instructions... Is that not obvious?

Re: LLMs get lost in multi-turn conversation

#203

Post by joleyj »

jsnider3 wrote: post #158
root_axis wrote: post #67

Earlier quoted context omitted.

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.

If the solution were easy or obvious the problem would likely have already been solved no?

Re: LLMs get lost in multi-turn conversation

#204

Post by diggan »

johnisgood wrote: post #196
diggan wrote: post #190

Earlier quoted context omitted.

In terms of "does useless refactors I didn't ask for nor improved anything", my own ranked list goes something like: Gemini > Claude > GPT. I don't really experience this at all with various GPT models used via the API, but overall GPTs seems to stick to the system prompt way better than the rest. Clause does OK too, but Gemini is out of control and writes soo much code and does so much you didn't ask for, really act…

The first time I used Claude, it rewrote >1k LOC without asking for it, but in retrospect, I was "using it wrong". With GPT, even when I told it to not do it, it still did that, but that was some time ago and it was not done via the API, so I dunno. I think I do agree with your list, but I haven't used Gemini that much. Yeah, they do come across as "overly eager junior devs", good comparison. :D

> With GPT, even when I told it to not do it, it still did that, but that was some time ago and it was not done via the API, so I dunno.

Personally I think it's a lot better via the API than ChatGPT. ChatGPT doesn't let you edit the "system prompt" which is really where you wanna put "how to" instructions, so it really follows them. Instructions put in the user message aren't followed as closely as when you use the system prompt, so probably why it still did something, if you were using ChatGPT.

Re: LLMs get lost in multi-turn conversation

#205

Post by oaeirjtlj »

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.

And now that chatgpt has a "memory" and can access previous conversations, it might be poisoned permanently. It gets one really bad idea, and forever after it insists on dumping that bad idea into every subsequent response ever after you repeatedly tell it "THAT'S A SHIT IDEA DON'T EVER MENTION THAT AGAIN". Sometimes it'll accidentally include some of its internal prompting, "user is very unhappy, make sure to not include xyz", and then it'll give you a response that is entirely focused around xyz.

Re: LLMs get lost in multi-turn conversation

#206

Post by forgotTheLast »

diggan wrote: post #184
dr_dshiv wrote: post #180

Earlier quoted context omitted.

The #1 tip I teach is to make extensive use of the teeny-tiny mostly hidden “edit” button in ChatGPT and Claude. When you get a bad response, stop and edit to get a better one, rather than letting crap start to multiply crap.

Hear hear! Basically if the first reply isn't good/didnt understand/got something wrong, restart from the beginning with a better prompt, explaining more/better. Rinse and repeat.

You can do even better by asking it to ask clarifying questions before generating anything, then editing your initial prompt with those clarifications.

Re: LLMs get lost in multi-turn conversation

#207

Post by airylizard »

thegeomaster wrote: post #156
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

Hey, thanks for kicking the tires! The run you’re describing was done in mid-April, right after GPT-4.1 went live. Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss.

If you reran today you’d see the same improved pass rate I’m getting now. That’s the downside of benchmarking against latest model names; behaviour changes quietly unless you pin to a dated snapshot.

For bigger, noisier prompts (or on GPT-3.5-turbo, which hasn’t changed) TSCE still gives a solid uplift, so the framework’s value stands. Appreciate you checking it out!

Re: LLMs get lost in multi-turn conversation

#208

Post by sitkack »

sandspar wrote: post #65
sitkack wrote: post #39

Earlier quoted context omitted.

You are just doubling down on protecting your argument. I operate LLMs in many conversational modes where it does ask clarifying questions, probing questions, baseline determining questions. It takes at most one sentence in the prompt to get them to act this way.

Could you share your prompt to get it to ask clarifying questions? I'm wondering if it would work in custom instructions.

It is domain dependent, you really need to play with it. Tell it you are doing pair thinking and either get it to ask questions about things it doesn't understand, or get it to ask you questions to get you to think better. Project the AI into a vantage point in the latent space and then get it to behave in the way that you want it to.

You can ask it to use the Socratic method, but then it is probing you, not its own understanding. Now have it use the socratic method on itself. You can tell it to have multiple simultaneous minds.

Play with deepseek in thinking and non-thinking mode, give it nebulous prompts and see if you can get it to ask for clarifications.

Re: LLMs get lost in multi-turn conversation

#209

Post by thegeomaster »

airylizard wrote: post #207
thegeomaster wrote: post #156

Earlier quoted context omitted.

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

Hey, thanks for kicking the tires! The run you’re describing was done in mid-April, right after GPT-4.1 went live. Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss. If you reran today you’d see the same improved pass rate I’m getting now. That’s the downside of benchmarking against latest model names; behaviour changes quietly un…

> Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss.

I don't know where you are getting this information from... The only snapshot of gpt-4.1 is gpt-4.1-2025-04-14 (mid-April), and the gpt-4.1 alias still points to it [1].

Just to be sure, I re-ran my test specifying that particular snapshot and am still getting a 100% pass rate.

[1]: https://platform.openai.com/docs/models/gpt-4.1

Re: LLMs get lost in multi-turn conversation

#210

Post by CobrastanJorji »

ezst wrote: post #70
CobrastanJorji wrote: post #62

Earlier quoted context omitted.

An interesting little example of this problem is initial prompting, which is effectively just a permanent, hidden context that can't be cleared. On Twitter right now, the "Grok" bot has recently begun frequently mentioning "White Genocide," which is, y'know, odd. This is almost certainly because someone recently adjusted its prompt to tell it what its views on white genocide are meant to be, which for a perfect chatb…

The heck??

Yeah, things are a little weird on Twitter these days. https://www.nbcnews.com/tech/tech-news/elon-musks-ai-chatbot...
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