title: ProgCo: Program Helps Self-Correction of Large Language Models
summary: Self-Correction aims to enable large language models (LLMs) to self-verify
  and self-refine their initial responses without external feedback. However,
  LLMs often fail to effectively self-verify and generate correct feedback,
  further misleading refinement and leading to the failure of self-correction,
  especially in complex reasoning tasks. In this paper, we propose Program-driven
  Self-Correction (ProgCo). First, program-driven verification (ProgVe) achieves
  complex verification logic and extensive validation through self-generated,
  self-executing verification pseudo-programs. Then, program-driven refinement
  (ProgRe) receives feedback from ProgVe, conducts dual reflection and refinement
  on both responses and verification programs to mitigate misleading of incorrect
  feedback in complex reasoning tasks. Experiments on three instruction-following
  and mathematical benchmarks indicate that ProgCo achieves effective
  self-correction, and can be further enhance performance when combined with real
  program tools.
paper_authors._id: 677751f23308d0c478a26abe
paper_authors.name: Xiaoshuai Song
paper_authors._id: 677751f23308d0c478a26abf
paper_authors.name: Yanan Wu
paper_authors._id: 677751f23308d0c478a26ac0
paper_authors.name: Weixun Wang
paper_authors._id: 677751f23308d0c478a26ac1
paper_authors.name: Jiaheng Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-05T23:02:58.578Z
paper_authors._id: 65377c30e48353201e6fdda0
paper_authors.avatarUrl: /avatars/a8f803b6f2e598eaee9c52c0d2ddfc16.svg
paper_authors.fullname: Jiaheng Liu
paper_authors.type: user
paper_authors.user: CheeryLJH
paper_authors._id: 677751f23308d0c478a26ac2
paper_authors.name: Wenbo Su
paper_authors._id: 677751f23308d0c478a26ac3
paper_authors.name: Bo Zheng
paper_title: ProgCo: Program Helps Self-Correction of Large Language Models
paper_summary: Self-Correction aims to enable large language models (LLMs) to self-verify
  and self-refine their initial responses without external feedback. However,
  LLMs often fail to effectively self-verify and generate correct feedback,
  further misleading refinement and leading to the failure of self-correction,
  especially in complex reasoning tasks. In this paper, we propose Program-driven
  Self-Correction (ProgCo). First, program-driven verification (ProgVe) achieves
  complex verification logic and extensive validation through self-generated,
  self-executing verification pseudo-programs. Then, program-driven refinement
  (ProgRe) receives feedback from ProgVe, conducts dual reflection and refinement
  on both responses and verification programs to mitigate misleading of incorrect
  feedback in complex reasoning tasks. Experiments on three instruction-following
  and mathematical benchmarks indicate that ProgCo achieves effective
  self-correction, and can be further enhance performance when combined with real
  program tools.
paper_ai_summary: Program-driven Self-Correction (ProgCo) improves large language models' self-verification and refinement by using self-generated and self-executing verification pseudo-programs, particularly in complex reasoning tasks.
paper_ai_keywords: Large language models (LLMs) self-verification self-refinement Program-driven Self-Correction (ProgCo) program-driven verification (ProgVe) program-driven refinement (ProgRe) dual reflection