Self-Discover: Large Language Models Self-Compose Reasoning Structures
Pei Zhou Jay Pujara Xiang Ren Xinyun Chen Heng-Tze Cheng Quoc V. Le Ed H. Chi Denny Zhou Swaroop Mishra Huaixiu Steven Zheng
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.