Summary of Self-discover: Large Language Models Self-compose Reasoning Structures, by Pei Zhou et al.
Self-Discover: Large Language Models Self-Compose Reasoning Structures
by Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The SELF-DISCOVER framework enables Large Language Models (LLMs) to autonomously identify the reasoning structures required for complex problem-solving. By selecting atomic modules like critical thinking and step-by-step thinking, LLMs compose explicit reasoning paths during decoding. This approach significantly improves performance on challenging benchmarks like BigBench-Hard, grounded agent reasoning, and MATH by up to 32% compared to Chain of Thought (CoT). SELF-DISCOVER also outperforms inference-intensive methods like CoT-Self-Consistency by over 20%, while requiring 10-40 times fewer computations. The framework’s self-discovered structures are universally applicable across model families, sharing commonalities with human reasoning patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SELF-DISCOVER is a new way for computers to figure out how to solve tricky problems. Right now, these problem-solving AI models can’t always get the right answer because they don’t know which steps to take to get there. This framework helps them find the best path by letting them choose from different thinking styles like critical thinking and step-by-step thinking. By combining these styles into a plan, the AI model can solve problems more accurately than before. It even works better than other methods that try to guess the right answer, while using much less computer power. |
Keywords
» Artificial intelligence » Inference
