2 ms·

Tree-of-Thought: Improving GPT-4 Reasoning by 900%

6 points by jumploops 3y ago

jumploops 3y ago
Paper here: https://arxiv.org/pdf/2305.10601.pdf https://arxiv.org/pdf/2305.10601.pdf
- Terretta 3y ago
  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. This shows the benefits of ToT's ability to explore, evaluate and search through different reasoning paths. The key benefits of ToT are: - Generality: It generalizes existing prompting methods - Modularity: The components can be varied independently - Adaptability: It can accommodate different problem properties and resource constraints - Convenience: It only requires a pre-trained language model The ToT framework shows how deliberate search through a tree of "thoughts" can help large language models solve problems that require planning and search, beyond their standard left-to-right generation process. Code to be (isn't yet) released at: https://github.com/ysymyth/tree-of-thought-llm https://github.com/ysymyth/tree-of-thought-llm // via: https://kagi.com/summarizer/index.html?url=https%3A%2F%2Farxiv.org%2Fpdf%2F2305.10601.pdf&target_language=&summary=summary https://kagi.com/summarizer/index.html?url=https%3A%2F%2Farx...

6 points by jumploops 3y ago

jumploops 3y ago

Paper here: https://arxiv.org/pdf/2305.10601.pdf https://arxiv.org/pdf/2305.10601.pdf

Terretta 3y ago
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. This shows the benefits of ToT's ability to explore, evaluate and search through different reasoning paths. The key benefits of ToT are: - Generality: It generalizes existing prompting methods - Modularity: The components can be varied independently - Adaptability: It can accommodate different problem properties and resource constraints - Convenience: It only requires a pre-trained language model The ToT framework shows how deliberate search through a tree of "thoughts" can help large language models solve problems that require planning and search, beyond their standard left-to-right generation process. Code to be (isn't yet) released at: https://github.com/ysymyth/tree-of-thought-llm https://github.com/ysymyth/tree-of-thought-llm // via: https://kagi.com/summarizer/index.html?url=https%3A%2F%2Farxiv.org%2Fpdf%2F2305.10601.pdf&target_language=&summary=summary https://kagi.com/summarizer/index.html?url=https%3A%2F%2Farx...