title: Self-Taught Self-Correction for Small Language Models
summary: Although large language models (LLMs) have achieved remarkable performance
  across various tasks, they remain prone to errors. A key challenge is enabling
  them to self-correct. While prior research has relied on external tools or
  large proprietary models, this work explores self-correction in small language
  models (SLMs) through iterative fine-tuning using solely self-generated data.
  We introduce the Self-Taught Self-Correction (STaSC) algorithm, which
  incorporates multiple algorithmic design choices. Experimental results on a
  question-answering task demonstrate that STaSC effectively learns
  self-correction, leading to significant performance improvements. Our analysis
  further provides insights into the mechanisms of self-correction and the impact
  of different design choices on learning dynamics and overall performance. To
  support future research, we release our user-friendly codebase and lightweight
  models.
paper_authors._id: 67d2c04b5d0737ce36ec58c8
paper_authors.name: Viktor Moskvoretskii
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-14T07:26:40.686Z
paper_authors._id: 63bbfd74141c7d395c471768
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673264437106-noauth.jpeg
paper_authors.fullname: Viktor Moskvoretskii
paper_authors.type: user
paper_authors.user: VityaVitalich
paper_authors._id: 67d2c04b5d0737ce36ec58c9
paper_authors.name: Chris Biemann
paper_authors._id: 67d2c04b5d0737ce36ec58ca
paper_authors.name: Irina Nikishina
paper_title: Self-Taught Self-Correction for Small Language Models
paper_summary: Although large language models (LLMs) have achieved remarkable performance
  across various tasks, they remain prone to errors. A key challenge is enabling
  them to self-correct. While prior research has relied on external tools or
  large proprietary models, this work explores self-correction in small language
  models (SLMs) through iterative fine-tuning using solely self-generated data.
  We introduce the Self-Taught Self-Correction (STaSC) algorithm, which
  incorporates multiple algorithmic design choices. Experimental results on a
  question-answering task demonstrate that STaSC effectively learns
  self-correction, leading to significant performance improvements. Our analysis
  further provides insights into the mechanisms of self-correction and the impact
  of different design choices on learning dynamics and overall performance. To
  support future research, we release our user-friendly codebase and lightweight
  models.
paper_ai_keywords: large language models (LLMs) small language models (SLMs) self-correct iterative fine-tuning self-generated data Self-Taught Self-Correction (STaSC) algorithmic design choices question-answering task self-correction learning dynamics