mlsanityData to 14 Oct 2026

Instruction Tuning for Large Language Models: A Survey

arXiv 2308.10792 ↗First posted 21 Aug 2023cs.CL cs.AI cs.LGcited by 623

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Cited by 69 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT)footnoteIn this paper, unless specified otherwise, supervised fine-tuning (SFT) and instruction tuning (IT) are used interchangeably., a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a dataset consisting of textsc(instruction, output) pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of SFT, the construction of SFT datasets, the training of SFT models, and applications to different modalities, domains and application, along with analysis on aspects that influence the outcome of SFT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research. Project Page: github.com/xiaoya-li/Instruction-Tuning-Survey

Key facts. Cited by 623 papers in the map (data through 14 Oct 2026). Builds on 128 of its 209 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

128 of 209 references.

References · 128

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
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  18. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
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  21. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  22. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  23. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  24. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,711
  25. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
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Citing papers per year: 2023: 50; 2024: 260; 2025: 219; 2026: 94.

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  8. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
  9. 2024MoE-LLaVA: Mixture of Experts for Large Vision-Language Modelscited by 300
  10. 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
  11. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Modelscited by 293
  12. 2023FireAct: Toward Language Agent Fine-tuningcited by 256
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  2. 2024OR-Bench: An Over-Refusal Benchmark for Large Language Modelscited by 244
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  4. 2023SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scalingcited by 172
  5. 2024Large Language Model-Brained GUI Agents: A Surveycited by 162
  6. 2024GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilitiescited by 156
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  8. 2024FinBen: A Holistic Financial Benchmark for Large Language Modelscited by 141
  9. 2024LoRA-GA: Low-Rank Adaptation with Gradient Approximationcited by 141
  10. 2024Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systemscited by 133
  11. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  12. 2025On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectificationcited by 127
  13. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  14. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGcited by 123
  15. 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 121
  16. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 116
  17. 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applicationscited by 111
  18. 2025What Can RL Bring to VLA Generalization? An Empirical Studycited by 108
  19. 2023Jatmo: Prompt Injection Defense by Task-Specific Finetuningcited by 106
  20. 2024Best Practices and Lessons Learned on Synthetic Datacited by 106
  21. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 99
  22. 2025Multiagent Finetuning: Self Improvement with Diverse Reasoning Chainscited by 95
  23. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 93
  24. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  25. 2024VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Modelscited by 87
  26. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  27. 2024FineSurE: Fine-grained Summarization Evaluation using LLMscited by 83
  28. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 79
  29. 2024A Closer Look at the Limitations of Instruction Tuningcited by 77
  30. 2024A Survey on Data Selection for LLM Instruction Tuningcited by 77
  31. 2024Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Modelscited by 73
  32. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 71
  33. 2024Reinforcement Learning Enhanced LLMs: A Surveycited by 71
  34. 2024In-context Learning with Retrieved Demonstrations for Language Models: A Surveycited by 70
  35. 2024Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedbackcited by 66
  36. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  37. 2024Instruct-Imagen: Image Generation with Multi-modal Instructioncited by 65
  38. 2024Instruction Backdoor Attacks Against Customized LLMscited by 62

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