51–60 of 158 posts
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Telling LLMs not to hallucinate in their prompt improves the output. https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-t...
Just because Apple includes it in one of their prompts doesn't mean it improves performance.
This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…
Did they maybe expand to a tree during training to learn more robust reasoning? Maybe. But it still comes down to a regular transformer model at inference time.
This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…
OAI revealed on Twitter that there is no "system" at inference time, this is just a model. Did they maybe expand to a tree during training to learn more robust reasoning? Maybe. But it still comes down to a regular transformer model at inference time.
This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…
You will not unlock "o1-like" reasoning by making a model think step by step. This is an old trick that people were using on GPT3 in 2020. If it were that simple, it wouldn't have taken OpenAI so long to release it.
Additionally, some of the prompt seems counterproductive:
>Be aware of your limitations as an llm and what you can and cannot do.
The LLM doesn't have a good idea of its limitations (any more than humans do). I expect this will create false refusals, as the model becomes overcautious.
>In all-caps to improve prompt compliance by emphesizing the importance of the instruction This kind of thing is still so funny to me. I wonder if the first guy who gets AGI to work will do it by realizing that he can improve LLM reliability over some threshold by telling it in all caps that his pet's life depends on the answer.
Telling LLMs not to hallucinate in their prompt improves the output. https://arstechnica.com/gadgets/2024/08/do-not-hallucinate-t...
Sort of like how offering to pay the LLM $5 improves its output. The LLM's taking your prompt seriously, but not literally.
Earlier quoted context omitted.
Just because Apple includes it in one of their prompts doesn't mean it improves performance.
I’ve had pretty good experience with it personally. It quite often just tells me it doesn’t know or isn’t sure instead of just making something up.
This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…
Came here hoping to find this. You will not unlock "o1-like" reasoning by making a model think step by step. This is an old trick that people were using on GPT3 in 2020. If it were that simple, it wouldn't have taken OpenAI so long to release it. Additionally, some of the prompt seems counterproductive: >Be aware of your limitations as an llm and what you can and cannot do. The LLM doesn't have a good idea of its lim…
Can it not be trained to do so? From my anecdotal observations, the knowledge cutoff is one thing that LLMs are really well trained to know about. Those are limitations that LLMs are currently well trained to handle. Why can it not be trained to know that it is quite frequently bad at math, it may produce sometimes inaccurate code etc.
For humans also, some people know some things are just not their cup of tea. Sure there are times people may have half baked knowledge about things but one can tell if they are good at XYZ things, and not so much at other things.
o1’s innovation is not Chain-of-Thought. It’s teaching the model to do CoT well (from massive amounts of human feedback) instead of just pretending to. You’ll never get o1 performance just from prompt engineering.
Earlier quoted context omitted.
OAI revealed on Twitter that there is no "system" at inference time, this is just a model. Did they maybe expand to a tree during training to learn more robust reasoning? Maybe. But it still comes down to a regular transformer model at inference time.
Source?
Did you benchmark your system against MMLU-pro?