mlsanityData to 7 Oct 2026

Learning or Self-aligning? Rethinking Instruction Fine-tuning

arXiv 2402.18243 ↗First posted 28 Feb 2024cs.CLcited by 43

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 19 of its 36 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

19 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Rethinking Data Selection for Supervised Fine-Tuning8 Feb
  2. 2023LLaMA: Open and Efficient Foundation Language Models27 Feb

Other references · 17

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Mistral 7Bcited by 3,065
  3. 2022A Survey on In-context Learningcited by 717
  4. 2023Instruction Tuning with GPT-4cited by 676
  5. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  6. 2024Self-Rewarding Language Modelscited by 654
  7. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 641
  8. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  9. 2023The False Promise of Imitating Proprietary LLMscited by 254
  10. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
  11. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
  12. 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 106
5 more
  1. 2024Tuning Language Models by Proxycited by 88
  2. 2023Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuningcited by 56
  3. 2024Human-Instruction-Free LLM Self-Alignment with Limited Samplescited by 25
  4. 2023Ambiguity-Aware In-Context Learning with Large Language Modelscited by 11
  5. 2024Knowledge Verification to Nip Hallucination in the Budcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 14; 2025: 20; 2026: 9.

43 papers.

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  1. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 227
  2. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  3. 2025Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognitioncited by 61
  4. 2024Knowledge Mechanisms in Large Language Models: A Survey and Perspectivecited by 50
  5. 2024Scaling Laws for Fact Memorization of Large Language Modelscited by 33
  6. 2025Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detectioncited by 19
  7. 2024Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimizationcited by 16
  8. 2025Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulationcited by 13
  9. 2024BianCang: A Traditional Chinese Medicine Large Language Modelcited by 11
  10. 2024TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Usecited by 11
  11. 2025GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigationcited by 11
  12. 2024Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuningcited by 10
31 more
  1. 2025DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modificationcited by 10
  2. 2025ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learningcited by 9
  3. 2026Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMscited by 9
  4. 2025Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levelscited by 7
  5. 2025SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Modelscited by 7
  6. 2024Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignmentcited by 6
  7. 2025Knowledge Augmented Complex Problem Solving with Large Language Models: A Surveycited by 6
  8. 2025Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewardscited by 6
  9. 2025Revealing the Power of Post-Training for Small Language Models via Knowledge Distillationcited by 6
  10. 2024Focus On This, Not That! Steering LLMs with Adaptive Feature Specificationcited by 5
  11. 2025A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognitioncited by 5
  12. 2025KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performancecited by 3
  13. 2024Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Managementcited by 2
  14. 202460 Data Points are Sufficient to Fine-Tune LLMs for Question-Answeringcited by 2
  15. 2024NILE: Internal Consistency Alignment in Large Language Modelscited by 2
  16. 2025Fine-Tuning on Noisy Instructions: Effects on Generalization and Performancecited by 2
  17. 2026A Layer-wise Analysis of Supervised Fine-Tuningcited by 2
  18. 2024Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packscited by 1
  19. 2025FairSense-AI: Responsible AI Meets Sustainabilitycited by 1
  20. 2025Explainable ICD Coding via Entity Linkingcited by 1
  21. 2025Resolving Knowledge Conflicts in Domain-specific Data Selection: A Case Study on Medical Instruction-tuningcited by 1
  22. 2026Understanding Why Language Models Hallucinate: Testing Reasoning Against Priorscited by 1
  23. 2025Self-supervised Attribute-aware Dynamic Preference Ranking Alignmentcited by 0
  24. 2025Towards Automatic Continual Learning: A Self-Adaptive Framework for Continual Instruction Tuningcited by 0
  25. 2025Does This Look Familiar to You? Knowledge Analysis via Model Internal Representationscited by 0
  26. 2026How to Utilize Complementary Vision-Text Information for 2D Structure Understandingcited by 0
  27. 2026Is One-Shot In-Context Learning Helpful for Data Selection in Task-Specific Fine-Tuning of Multimodal LLMs?cited by 0
  28. 2026What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"cited by 0
  29. 2026IRIS: Interpolative R'enyi Iterative Self-play for Large Language Model Fine-Tuningcited by 0
  30. 2026Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuningcited by 0
  31. 2026Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfercited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Emergence of Context Characteristics Sensitivity in Large Language Models
  2. 2024Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
  3. 2023Dynamics of Instruction Fine-Tuning for Chinese Large Language Models
  4. 2024Revisiting the Superficial Alignment Hypothesis
  5. 2024Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
  6. 2023Instruction-tuning Aligns LLMs to the Human Brain
  7. 2025Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective
  8. 2023A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment