Self-Discover: Large Language Models Self-Compose Reasoning Structures
Authors: name: Pei Zhou; name: Jay Pujara; name: Xiang Ren; name: Xinyun Chen; name: Heng-Tze Cheng; name: Quoc V. Le; name: Ed H. Chi; name: Denny Zhou; name: Swaroop Mishra; name: Huaixiu Steven Zheng
Published: 2024-02-06 01:13:53

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.