Halting is sometimes preferable to thrashing around and running in circles.
I feel like if LLMs "knew" when they're out of their depth, they could be much more useful. The question is whether knowing when to stop can be meaningfully learned from examples with RL. From all we've seen the hallucination problem and this stopping problem all boil down to this problem that you could teach the model to say "I don't know" but if that's part of the training dataset it might just spit out "I don't know" to random questions, because it's a likely response in the realm of possible responses, instead of spitting out "I don't know" to not knowing.
SocratesAI is still unsolved, and LLMs are probably not the path to get knowing that you know nothing.
I would consider that detecting your own limits when trying to solve a problem is preferable to having the illusion of thinking that your solution is working and correct.
Ah yes, the function that halts if the input problem would take too long to halt.
But yes, I assume you mean they abort their loop after a while, which they do.
This whole idea of a "reasoning benchmark" doesn't sit well with me. It seems still not well-defined to me.
Maybe it's just bias I have or my own lack of intelligence, but it seems to me that using language models for "reasoning" is still more or less a gimmick and convenience feature (to automate re-prompts, clarifications etc, as far as possible).
But reading this pop-sci article from summer 2022 seems like this definition problem hasn't changed very much since then.
Although it's about AI progress before ChatGPT and it doesn't even mention the GPT base models. Sure, some of the tasks mentioned in the article seem dated today.
But IMO, there is still no AI model that can be trusted to, for example, accurately summarize a Wikipedia article.
Not all humans can do that either, sure. But humans are better at knowing what they don't know, and deciding what other humans can be trusted. And of course, none of this is an arithmetic or calculation task.
p_v_doom|4 months ago
dspillett|4 months ago
carlmr|4 months ago
I feel like if LLMs "knew" when they're out of their depth, they could be much more useful. The question is whether knowing when to stop can be meaningfully learned from examples with RL. From all we've seen the hallucination problem and this stopping problem all boil down to this problem that you could teach the model to say "I don't know" but if that's part of the training dataset it might just spit out "I don't know" to random questions, because it's a likely response in the realm of possible responses, instead of spitting out "I don't know" to not knowing.
SocratesAI is still unsolved, and LLMs are probably not the path to get knowing that you know nothing.
LunaSea|4 months ago
benterix|4 months ago
moritzwarhier|4 months ago
But yes, I assume you mean they abort their loop after a while, which they do.
This whole idea of a "reasoning benchmark" doesn't sit well with me. It seems still not well-defined to me.
Maybe it's just bias I have or my own lack of intelligence, but it seems to me that using language models for "reasoning" is still more or less a gimmick and convenience feature (to automate re-prompts, clarifications etc, as far as possible).
But reading this pop-sci article from summer 2022 seems like this definition problem hasn't changed very much since then.
Although it's about AI progress before ChatGPT and it doesn't even mention the GPT base models. Sure, some of the tasks mentioned in the article seem dated today.
But IMO, there is still no AI model that can be trusted to, for example, accurately summarize a Wikipedia article.
Not all humans can do that either, sure. But humans are better at knowing what they don't know, and deciding what other humans can be trusted. And of course, none of this is an arithmetic or calculation task.
https://www.science.org/content/article/computers-ace-iq-tes...