mlsanityData to 7 Oct 2026

Semi-supervised Fine-tuning for Large Language Models

arXiv 2410.14745 ↗First posted 17 Oct 2024cs.CL cs.AIcited by 8

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Abstract (arXiv)

Supervised fine-tuning (SFT) is crucial in adapting large language model (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(SemiFT) task and a framework named SemiEvol for LLM alignment 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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 37 of its 66 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

37 of 66 references.

References · 37

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
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25 more
  1. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  3. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  4. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  5. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
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  7. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  8. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  9. 2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Modelscited by 552
  10. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 514
  11. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  12. 2013Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Textscited by 310
  13. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 194
  14. 2024Are self-explanations from Large Language Models faithful?cited by 101
  15. 2023Data Diversity Matters for Robust Instruction Tuningcited by 76
  16. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  17. 2024Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuningcited by 57
  18. 2023Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Taskscited by 53
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  24. 2024Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classificationcited by 2
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2025SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compressioncited by 9
  2. 2026Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalizationcited by 5
  3. 2025Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selectioncited by 4
  4. 2025A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarioscited by 4
  5. 2025UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speechcited by 2
  6. 2026Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphscited by 0
  7. 2026Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learningcited by 0
  8. 2026DECSELFMASK: Leveraging Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classificationcited by 0

Similar, not cited

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  1. 2025Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
  2. 2024Rethinking Data Selection at Scale: Random Selection is Almost All You Need
  3. 2024Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models
  4. 2024HFT: Half Fine-Tuning for Large Language Models
  5. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
  6. 2024L3Ms -- Lagrange Large Language Models
  7. 2025Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
  8. 2024Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models