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0 pointsACCount377mo ago0 comments

1-turn instruction following and multi-turn instruction following are not the same exact capability, and some AIs only "get good" at the former. 1-turn gets more training attention - because it's more noticeable, in casual use and benchmarks both, and also easier to train for.

With weak multi-turn instruction following, context data will often dominate over user instructions. Resulting in very "loopy" AI - and more sessions that are easier to restart from scratch than to "fix".

Gemini is notorious for underperforming at this, while Claude has relatively good performance. I expect that many models from lesser known providers would also have a multi-turn instruction following gap.

0 comments

vidarh7mo ago

This is a good point, and to drive this home to people, if you have a conversation of this pattern:

    User: Fix this problem ...
    Assistant: X
    User: No, don't do X
    Assistant: Y
    User: No, Y is wrong too.
    Assistant: X

It is generally pointless to continue. You now have a context that is full of the assistant explaining to you and itself why X and Y are the right answers, and much less context of you explaining why it is wrong.

If you reach that state, start over, and constrain your initial request to exclude X and Y. If it brings up either again, start over, and constrain your request further.

If the model is bad at handling multiple turns without getting into a loop, telling it that it is wrong is not generally going to achieve anything, but starting over with better instructions often will.

I see so many people get stuck "arguing" with a model over this, getting more and more frustrated as the model keeps repeating variations of the broken answer, without realising they're filling the context with arguments from the model for why the broken answer is right.

recursive7mo ago

This is also a thing that's bad about LLMs. You're holding it wrong if you continue to argue. But LLMs are presented as if we can use the conventions of natural language to communicate with them. That's how they're sold. So if they fail to live up to those expectations, that's still a problem with LLMs.

vidarh7mo ago

It's a problem with LLM's and people are "holding it wrong".

It makes zero difference that they've been sold as doing better if other people learn how to use them effectively and I choose to ignore how to get the best possible results out of them.

1 more reply

xienze7mo ago

> I see so many people get stuck "arguing" with a model over this, getting more and more frustrated as the model keeps repeating variations of the broken answer

Maybe because people expect AI systems that are touted as all-knowing, all-powerful, coming-for-your-job to be smart enough to remember what was said two turns ago?

vidarh7mo ago

That's fine once or twice. At that point people should learn that this isn't how they work, and figure out how to use them better.

It's not a tools fault if people insist on continuing to use them in counter-productive ways.

2 more replies

psadauskas7mo ago

There's also the Pink Elephant Paradox (Whatever you do, DO NOT think about a pink elephant).

If you mention X or Y, even if they're preceded by "DO NOT" in all caps, an LLM will still end up with both X and Y into its context, making it more likely it gets used.

I'm running out of ways to tell the assistant to not use mocks for tests, it really really wants to use them.

vidarh7mo ago

I think in some cases you "just" need to instead up temperature to increase the variety of responses, repeat requests, and use hooks to automatically review and reject bad options.

(And yes, it's a horrible workaround)

_flux7mo ago

Indeed, arguing with LLM is good if you like arguing. For results it's not the way to go.

I think often it's not required to completely start over: just identify the part where it goes off the rails, and modify your prompt just before that point. But yeah, basically the same process.

vidarh7mo ago

Sure, when working with tools - like Copilot for example - that lets you "restore" the conversation to a given point, that has pretty much the same effect. The key is to excise the "bad steps" from the conversation and figure out how to amend the next conversation steps so it doesn't veer off in the wrong direction.

dingnuts7mo ago

I don't know why this could be the case but I have absolutely gotten better results out of the bot after insulting it.

QuantumGood7mo ago

I always ask "Tell me what you think it is I am asking" before asking for a solution. Improves the solution and context.

rtkwe7mo ago

I have this problem all the time with minor image edits on ChatGPT the few time's I've tried it. Any time I try to do a second edit or change to the generated image it seems to take the already degraded output from it's first attempt and use that instead of the original image.

j / k navigate · click thread line to collapse

0 comments

vidarh7mo ago

This is a good point, and to drive this home to people, if you have a conversation of this pattern:

    User: Fix this problem ...
    Assistant: X
    User: No, don't do X
    Assistant: Y
    User: No, Y is wrong too.
    Assistant: X

If you reach that state, start over, and constrain your initial request to exclude X and Y. If it brings up either again, start over, and constrain your request further.

recursive7mo ago

vidarh7mo ago

It's a problem with LLM's and people are "holding it wrong".

It makes zero difference that they've been sold as doing better if other people learn how to use them effectively and I choose to ignore how to get the best possible results out of them.

1 more reply

xienze7mo ago

> I see so many people get stuck "arguing" with a model over this, getting more and more frustrated as the model keeps repeating variations of the broken answer

Maybe because people expect AI systems that are touted as all-knowing, all-powerful, coming-for-your-job to be smart enough to remember what was said two turns ago?

vidarh7mo ago

That's fine once or twice. At that point people should learn that this isn't how they work, and figure out how to use them better.

It's not a tools fault if people insist on continuing to use them in counter-productive ways.

2 more replies

psadauskas7mo ago

There's also the Pink Elephant Paradox (Whatever you do, DO NOT think about a pink elephant).

If you mention X or Y, even if they're preceded by "DO NOT" in all caps, an LLM will still end up with both X and Y into its context, making it more likely it gets used.

I'm running out of ways to tell the assistant to not use mocks for tests, it really really wants to use them.

vidarh7mo ago

I think in some cases you "just" need to instead up temperature to increase the variety of responses, repeat requests, and use hooks to automatically review and reject bad options.

(And yes, it's a horrible workaround)

_flux7mo ago

Indeed, arguing with LLM is good if you like arguing. For results it's not the way to go.

I think often it's not required to completely start over: just identify the part where it goes off the rails, and modify your prompt just before that point. But yeah, basically the same process.

vidarh7mo ago

dingnuts7mo ago

I don't know why this could be the case but I have absolutely gotten better results out of the bot after insulting it.

QuantumGood7mo ago

I always ask "Tell me what you think it is I am asking" before asking for a solution. Improves the solution and context.

rtkwe7mo ago

j / k navigate · click thread line to collapse