title: SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation
summary: Supervised fine-tuning (SFT) is crucial in adapting large language models
  (LLMs) to a specific domain or task. However, only a limited amount of labeled
  data is available in practical applications, which poses a severe challenge for
  SFT in yielding satisfactory results. Therefore, a data-efficient framework
  that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly
  anticipated. Towards this end, we introduce a semi-supervised fine-tuning
  framework named SemiEvol for LLM adaptation from a propagate-and-select manner.
  For knowledge propagation, SemiEvol adopts a bi-level approach, propagating
  knowledge from labeled data to unlabeled data through both in-weight and
  in-context methods. For knowledge selection, SemiEvol incorporates a
  collaborative learning mechanism, selecting higher-quality pseudo-response
  samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven
  general or domain-specific datasets, demonstrating significant improvements in
  model performance on target data. Furthermore, we compared SemiEvol with SFT
  and self-evolution methods, highlighting its practicality in hybrid data
  scenarios.
paper_authors._id: 671701fe9ec0ff4d1501fa6c
paper_authors.name: Junyu Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-22T08:01:10.194Z
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paper_authors.fullname: junyu
paper_authors.type: user
paper_authors.user: luojunyu
paper_authors._id: 671701fe9ec0ff4d1501fa6d
paper_authors.name: Xiao Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-23T07:34:51.377Z
paper_authors._id: 67103e13377b0d6540e54a78
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pfpNTJG1A11ubZkukI-le.jpeg
paper_authors.fullname: PatriciaXiao
paper_authors.type: user
paper_authors.user: PatriciaXiao
paper_authors._id: 671701fe9ec0ff4d1501fa6e
paper_authors.name: Xiusi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T09:36:49.362Z
paper_authors._id: 6270ff726417aed8a7340c8b
paper_authors.avatarUrl: /avatars/3f14913c55cc4fc78678ac43fb603e80.svg
paper_authors.fullname: Xiusi Chen
paper_authors.type: user
paper_authors.user: XtremSup
paper_authors._id: 671701fe9ec0ff4d1501fa6f
paper_authors.name: Zhiping Xiao
paper_authors._id: 671701fe9ec0ff4d1501fa70
paper_authors.name: Wei Ju
paper_authors._id: 671701fe9ec0ff4d1501fa71
paper_authors.name: Ming Zhang
paper_title: SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation
paper_summary: Supervised fine-tuning (SFT) is crucial in adapting large language models
  (LLMs) to a specific domain or task. However, only a limited amount of labeled
  data is available in practical applications, which poses a severe challenge for
  SFT in yielding satisfactory results. Therefore, a data-efficient framework
  that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly
  anticipated. Towards this end, we introduce a semi-supervised fine-tuning
  framework named SemiEvol for LLM adaptation from a propagate-and-select manner.
  For knowledge propagation, SemiEvol adopts a bi-level approach, propagating
  knowledge from labeled data to unlabeled data through both in-weight and
  in-context methods. For knowledge selection, SemiEvol incorporates a
  collaborative learning mechanism, selecting higher-quality pseudo-response
  samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven
  general or domain-specific datasets, demonstrating significant improvements in
  model performance on target data. Furthermore, we compared SemiEvol with SFT
  and self-evolution methods, highlighting its practicality in hybrid data
  scenarios.
paper_ai_keywords: semi-supervised fine-tuning propagate-and-select bi-level approach in-weight in-context collaborative learning pseudo-response GPT-4o-mini Llama-3.1 hybrid data scenarios