Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
📰 ArXiv cs.AI
Learn to build dynamic reasoning frameworks for large language models using chains, trees, and graphs, and optimize them for better performance
Action Steps
- Build a Chain of Thought prompting scheme to enhance reasoning capabilities
- Implement a Tree of Thoughts structure to handle complex problem types
- Configure a Graph of Thoughts framework to optimize hyperparameters and prompts
- Test and evaluate the performance of the framework on dynamic and unseen problem types
- Apply optimization techniques to reduce runtime and prompting cost
Who Needs to Know This
AI researchers and engineers can benefit from this framework to improve the reasoning capabilities of their language models, and software engineers can apply these principles to develop more efficient and adaptable AI systems
Key Insight
💡 Dynamic and optimized reasoning frameworks can significantly improve the performance of large language models
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🤖 Enhance your language model's reasoning with dynamic chains, trees, and graphs! 📈 Optimize for better performance and adaptability #AI #LLMs
Key Takeaways
Learn to build dynamic reasoning frameworks for large language models using chains, trees, and graphs, and optimize them for better performance
Full Article
Title: Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
Abstract:
arXiv:2602.16512v2 Announce Type: replace Abstract: Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning structures that lack adaptability to dynamic or unseen problem types. Additionally, these schemes are often under-optimized in terms of hyperparameters, prompts, runtime, and prompting cost. To a
Abstract:
arXiv:2602.16512v2 Announce Type: replace Abstract: Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning structures that lack adaptability to dynamic or unseen problem types. Additionally, these schemes are often under-optimized in terms of hyperparameters, prompts, runtime, and prompting cost. To a
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