mlsanityData to 7 Oct 2026

#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models

arXiv 2308.07074 ↗First posted 14 Aug 2023cs.CL cs.AI cs.LGcited by 108

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Abstract (arXiv)

Foundation language models obtain the instruction-following ability through supervised fine-tuning (SFT). Diversity and complexity are considered critical factors of a successful SFT dataset, while their definitions remain obscure and lack quantitative analyses. In this work, we propose InsTag, an open-set fine-grained tagger, to tag samples within SFT datasets based on semantics and intentions and define instruction diversity and complexity regarding tags. We obtain 6.6K tags to describe comprehensive user queries. Then we analyze popular open-sourced SFT datasets and find that the model ability grows with more diverse and complex data. Based on this observation, we propose a data selector based on InsTag to select 6K diverse and complex samples from open-source datasets and fine-tune models on InsTag-selected data. The resulting models, TagLM, outperform open-source models based on considerably larger SFT data evaluated by MT-Bench, echoing the importance of query diversity and complexity. We open-source InsTag in https://github.com/OFA-Sys/InsTag.

Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 14 Aug 2023.

Builds on

28 of 43 references.

References · 28

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  6. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  7. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  9. 2021Program Synthesis with Large Language Modelscited by 3,509
  10. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  11. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  12. 2023A Survey of Large Language Modelscited by 2,537
16 more
  1. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  2. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  3. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
  4. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  5. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  6. 2023Instruction Tuning with GPT-4cited by 676
  7. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  8. 2023How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resourcescited by 405
  9. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  10. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 369
  11. 2023Preference Ranking Optimization for Human Alignmentcited by 283
  12. 2023Specializing Smaller Language Models towards Multi-Step Reasoningcited by 249
  13. 2020Graph-based, Self-Supervised Program Repair from Diagnostic Feedbackcited by 93
  14. 2020Learning Dense Representations of Phrases at Scalecited by 91
  15. 2021Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Explorationcited by 44
  16. 2023Exploring Partial Knowledge Base Inference in Biomedical Entity Linkingcited by 2

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Citing papers per year: 2023: 4; 2024: 44; 2025: 44; 2026: 16.

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  1. 2023Qwen Technical Reportcited by 3,273
  2. 2024Qwen2 Technical Reportcited by 2,076
  3. 2024Yi: Open Foundation Models by 01.AIcited by 706
  4. 2023What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuningcited by 354
  5. 2024HelpSteer2: Open-source dataset for training top-performing reward modelscited by 232
  6. 2025DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Modelscited by 154
  7. 2024DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solvingcited by 92
  8. 2024Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuningcited by 85
  9. 2024A Survey on Data Selection for LLM Instruction Tuningcited by 76
  10. 2024SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selectioncited by 64
  11. 2024A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learningcited by 52
  12. 2025Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Modelscited by 49
38 more
  1. 2025Seed-Coder: Let the Code Model Curate Data for Itselfcited by 46
  2. 2024Entropy Law: The Story Behind Data Compression and LLM Performancecited by 42
  3. 2024HelloBench: Evaluating Long Text Generation Capabilities of Large Language Modelscited by 42
  4. 2025Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuningcited by 39
  5. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
  6. 2025Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoningcited by 38
  7. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  8. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Modelscited by 31
  9. 2025CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning taskscited by 30
  10. 2024Diversity Measurement and Subset Selection for Instruction Tuning Datasetscited by 28
  11. 2024Online Merging Optimizers for Boosting Rewards and Mitigating Tax in Alignmentcited by 28
  12. 2024TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Datacited by 28
  13. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  14. 2024Improving Data Efficiency via Curating LLM-Driven Rating Systemscited by 26
  15. 2023Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Taskcited by 23
  16. 2024What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?cited by 22
  17. 2024SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuningcited by 19
  18. 2024MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Modelscited by 17
  19. 2025Incentivizing Reasoning for Advanced Instruction-Following of Large Language Modelscited by 17
  20. 2024Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimizationcited by 16
  21. 2024Better Alignment with Instruction Back-and-Forth Translationcited by 16
  22. 2024CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMscited by 15
  23. 2024Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuningcited by 14
  24. 2024Exploring the Mystery of Influential Data for Mathematical Reasoningcited by 13
  25. 2024Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependencycited by 13
  26. 2025R.I.P.: Better Models by Survival of the Fittest Promptscited by 13
  27. 2025Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Datacited by 13
  28. 2025LEAD: Iterative Data Selection for Efficient LLM Instruction Tuningcited by 13
  29. 2024Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspectivecited by 12
  30. 2025BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuningcited by 12
  31. 2025Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencodercited by 11
  32. 2024Speculative Coreset Selection for Task-Specific Fine-tuningcited by 10
  33. 2024Compute-Constrained Data Selectioncited by 10
  34. 2024Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativenesscited by 10
  35. 2026Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewardscited by 10
  36. 2024Federated Data-Efficient Instruction Tuning for Large Language Modelscited by 9
  37. 2025AIR: A Systematic Analysis of Annotations, Instructions, and Response Pairs in Preference Datasetcited by 8
  38. 2025IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignmentcited by 8

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