I finally got access to it, I tried playing Connect 4 with it, but it didn't go very well. A bit disappointed.
Learning to Reason with LLMs
361–370 of 1001 posts
Re: Learning to Reason with LLMs
#362Here's the o1-preview answer to the strawberry question: -- There are *three* letter "R"s in the word "strawberry." Let's break down the word to count the occurrences: - *S* - *T* - *R* - *A* - *W* - *B* - *E* - *R* - *R* - *Y* The letter "R" appears in positions 3, 8, and 9.
Given that this is a well known example, presumably OpenAI included a training set using letters.
Re: Learning to Reason with LLMs
#363Very interesting. I guess this is the strawberry model that was rumoured. I am a bit surprised that this does not beat GPT-4o for personal writing tasks. My expectations would be that a model that is better at one thing is better across the board. But I suppose writing is not a task that generally requires "reasoning steps", and may also be difficult to evaluate objectively.
In the performance tests they said they used "consensus among 64 samples" and "re-ranking 1000 samples with a learned scoring function" for the best results. If they did something similar for these human evaluations, rather than just use the single sample, you could see how that would be horrible for personal writing.
I don’t understand how that is generalizable. I’m not going to be able to train a scoring function for any arbitrary task I need to do. In many cases the problem of ranking is at least as hard as generating a response in the first place.
Re: Learning to Reason with LLMs
#364Interesting sequence from the Cipher CoT: Third pair: 'dn' to 'i' 'd'=4, 'n'=14 Sum:4+14=18 Average:18/2=9 9 corresponds to 'i'(9='i') But 'i' is 9, so that seems off by 1. So perhaps we need to think carefully about letters. Wait, 18/2=9, 9 corresponds to 'I' So this works. ----- This looks like recovery from a hallucination. Is it realistic to expect CoT to be able to recover from hallucinations this quickly?
Did it hallucinate? I haven't looked at it, but lowercase i and uppercase i is not the same number if you're getting the number from ascii
Re: Learning to Reason with LLMs
#365Interesting to note, as an outside observer only keeping track of this stuff as a hobby, that it seems like most of OpenAI’s efforts to drive down compute costs per token and scale up context windows is likely being done in service of enabling larger and larger chains of thought and reasoning before the model predicts its final output tokens. The benefits of lower costs and larger contexts to API consumers and applications - which I had assumed to be the primary goal - seem likely to mostly be happy side effects.
This makes obvious sense in retrospect, since my own personal experiments with spinning up a recursive agent a few years ago using GPT-3 ran into issues with insufficient context length and loss of context as tokens needed to be discarded, which made the agent very unreliable. But I had not realized this until just now. I wonder what else is hiding in plain sight?
Re: Learning to Reason with LLMs
#366Yes, but it will hallucinate like all other LLM tech making it fully unreliable for anything mission critical. You literally need to know the answer to validate the output, because if you don't, you won't know if output is true or false or in between.
Re: Learning to Reason with LLMs
#367This is incredible. In April I used the standard GPT-4 model via ChatGPT to help me reverse engineer the binary bluetooth protocol used by my kitchen fan to integrate it into Home Assistant.
It was helpful in a rubber duck way, but could not determine the pattern used to transmit the remaining runtime of the fan in a certain mode. Initial prompt here [0]
I pasted the same prompt into o1-preview and o1-mini and both correctly understood and decoded the pattern using a slightly different method than I devised in April. Asking the models to determine if my code is equivalent to what they reverse engineered resulted in a nuanced and thorough examination, and eventual conclusion that it is equivalent. [1]
Testing the same prompt with gpt4o leads to the same result as April's GPT-4 (via ChatGPT) model.
Amazing progress.
[0]: https://pastebin.com/XZixQEM6
[1]: https://i.postimg.cc/VN1d2vRb/SCR-20240912-sdko.png (sorry about the screenshot – sharing ChatGPT chats is not easy)
Re: Learning to Reason with LLMs
#368Per-token billing will be lit
Re: Learning to Reason with LLMs
#369Here's the o1-preview answer to the strawberry question: -- There are *three* letter "R"s in the word "strawberry." Let's break down the word to count the occurrences: - *S* - *T* - *R* - *A* - *W* - *B* - *E* - *R* - *R* - *Y* The letter "R" appears in positions 3, 8, and 9.
can you ask it: 9.11 and 9.9, which number is larger
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Re: Learning to Reason with LLMs
#370"hidden chain of thought" is basically the finetuned prompt isn't it? The time scale x-axis is hidden as well. Not sure how they model the gpt for it to have an ability to decide when to stop CoT and actually answer.