Self-Discover: Large Language Models Self-Compose Reasoning Structures

• URL: http://arxiv.org/abs/2402.03620v1
• Date: Tue, 6 Feb 2024 01:13:53 GMT
• Title: Self-Discover: Large Language Models Self-Compose Reasoning Structures
• Authors: Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng
• Abstract summary: We introduce SELF-DISCOVER, a framework for self-discovering task-intrinsic reasoning structures. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks. We show that the self-discovered reasoning structures are universally applicable across model families.
• Score: 136.48389510481758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, and compose them into an explicit reasoning structure for LLMs to follow during decoding. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as much as 32% compared to Chain of Thought (CoT). Furthermore, SELF-DISCOVER outperforms inference-intensive methods such as CoT-Self-Consistency by more than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns.

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