tree-of-thought-llm

This dude has issues, the reddit post in /r/MachineLearning top comment: > Yes, basically. Delete any kyegomez link on sight. He namesquats recent papers for the clout, though the code never actually runs, much less replicates the paper results. We've had problems in /r/mlscaling with people unwittingly linking his garbage - we haven't bothered to set up an Automod rule, though. [0] https://github.com/princeton-nlp/tree-of-thought-llm/issues/... [1] https://x.com/ShunyuYao12/status/1663946702754021383

Isn't o1 just applying last year's Tree of Thoughts paper in production? Is there any reason to believe that the other companies will struggle to implement their own? https://github.com/princeton-nlp/tree-of-thought-llm

GPU utilization should be down when using this technique. I’m hoping this could allow for more efficient batch inference on GPUs. If you can predict 10 tokens for the price of 1 it should allow you to do tree of thought much more efficiently. https://github.com/princeton-nlp/tree-of-thought-llm

"Tree of Thoughts: Deliberate Problem Solving with Large Language Models" seems better than just re-rolling the dice. https://github.com/princeton-nlp/tree-of-thought-llm

This is Shunyu, author of Tree oF Thoughts (arxiv.org/abs/2305.10601). The official code to replicate paper results is https://github.com/ysymyth/tree-of-thought-llm Not https://github.com/kyegomez/tree-of-thoughts which according to many who told me, is not right/good implementation of ToT, cannot replicate paper results, and damages the reputation of ToT I explained the situation here: https://twitter.com/ShunyuYao12/status/1663946702754021383 I'd appreciate your help by unstaring his and staring mine, as currently Github and Google searches go to his repo by default, and it has been very misleading for many users. Thanks!

This is Shunyu, author of Tree oF Thoughts (arxiv.org/abs/2305.10601). The official code to replicate paper results is https://github.com/ysymyth/tree-of-thought-llm Not https://github.com/kyegomez/tree-of-thoughts which according to many who told me, is not right/good implementation of ToT, and damages the reputation of ToT I explained the situation here: https://twitter.com/ShunyuYao12/status/1663946702754021383 I'd appreciate your help by unstaring his and staring mine, as currently Github and Google searches go to his repo by default, and it has been very misleading for many users.

here is the repo by the paper author: https://github.com/ysymyth/tree-of-thought-llm

I'm a little confused about what the relation is (if any) between the OP link and the repo from that paper: https://github.com/ysymyth/tree-of-thought-llm Is it basically a reimplementation using Guidance instead of openai's API directly?

Here is a summary of the key points in the PDF: Large language models are increasingly being used for general problem solving, but they are still limited by their token-level, left-to-right generation process. The authors propose a new Tree of Thoughts (ToT) framework to address this. ToT frames problem solving as search through a tree of "thoughts", where each thought is an intermediate step towards the solution. This allows the language model to: - Generate and explore multiple potential thoughts at each step - Evaluate the progress of different thoughts using self-evaluation prompts - Perform lookahead and backtracking to make global decisions The authors propose 3 tasks to test ToT: Game of 24, Creative Writing and Crosswords. ToT significantly outperforms standard input-output and chain-of-thought prompting baselines on all 3 tasks.

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking a