title: Self-Discover: Large Language Models Self-Compose Reasoning Structures
summary: 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.
paper_authors._id: 65c2ef81c1ab7dcfc9594950
paper_authors.name: Pei Zhou
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-02-07T02:48:33.652Z
paper_authors._id: 60844fd056f7623a0b84914b
paper_authors.avatarUrl: /avatars/f7d2763258b8c614783ead9480fbcf8d.svg
paper_authors.fullname: Pei Zhou
paper_authors.type: user
paper_authors.user: peiz
paper_authors._id: 65c2ef81c1ab7dcfc9594951
paper_authors.name: Jay Pujara
paper_authors._id: 65c2ef81c1ab7dcfc9594952
paper_authors.name: Xiang Ren
paper_authors._id: 65c2ef81c1ab7dcfc9594953
paper_authors.name: Xinyun Chen
paper_authors._id: 65c2ef81c1ab7dcfc9594954
paper_authors.name: Heng-Tze Cheng
paper_authors._id: 65c2ef81c1ab7dcfc9594955
paper_authors.name: Quoc V. Le
paper_authors._id: 65c2ef81c1ab7dcfc9594956
paper_authors.name: Ed H. Chi
paper_authors._id: 65c2ef81c1ab7dcfc9594957
paper_authors.name: Denny Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-20T18:27:07.442Z
paper_authors._id: 65d4d62d493aeaf0bb295f1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_authors._id: 65c2ef81c1ab7dcfc9594958
paper_authors.name: Swaroop Mishra
paper_authors._id: 65c2ef81c1ab7dcfc9594959
paper_authors.name: Huaixiu Steven Zheng
paper_title: Self-Discover: Large Language Models Self-Compose Reasoning Structures
paper_summary: 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.
paper_ai_summary: SELF-DISCOVER is a framework enabling LLMs to generate task-specific reasoning structures, enhancing performance on complex reasoning tasks with reduced computational cost.
paper_ai_keywords: SELF-DISCOVER LLMs self-discovery process atomic reasoning modules critical thinking step-by-step thinking BigBench-Hard grounded agent reasoning MATH Chain of Thought (CoT) CoT-Self-Consistency Llama2