mlsanityData to 7 Oct 2026

A Closer Look at the Limitations of Instruction Tuning

arXiv 2402.05119 ↗First posted 3 Feb 2024cs.CL cs.AIcited by 76

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Abstract (arXiv)

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limited to learning response initiation and style tokens, and full-parameter fine-tuning leads to knowledge degradation. (2) Copying response patterns from IT datasets derived from knowledgeable sources leads to a decline in response quality. (3) Full-parameter fine-tuning increases hallucination by inaccurately borrowing tokens from conceptually similar instances in the IT dataset for generating responses. (4) Popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets. We hope the insights and challenges revealed in this paper inspire future work in related directions.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 3 Feb 2024.

Builds on

23 of 35 references.

References · 23

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  7. 2023Mistral 7Bcited by 3,065
  8. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  9. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  10. 2023A Survey of Large Language Modelscited by 2,537
  11. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  12. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
11 more
  1. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 702
  2. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  3. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 559
  4. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
  5. 2023Catastrophic Jailbreak of Open-source LLMs via Exploiting Generationcited by 439
  6. 2023The False Promise of Imitating Proprietary LLMscited by 254
  7. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learningcited by 251
  8. 2023Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2cited by 227
  9. 2023NEFTune: Noisy Embeddings Improve Instruction Finetuningcited by 99
  10. 2023AlpaCare:Instruction-tuned Large Language Models for Medical Applicationcited by 88
  11. 2023Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuningcited by 48

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Citing papers per year: 2024: 22; 2025: 40; 2026: 14.

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  1. 2024LoRA Learns Less and Forgets Lesscited by 313
  2. 2025LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!cited by 132
  3. 2025Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilitiescited by 127
  4. 2024LoRA vs Full Fine-tuning: An Illusion of Equivalencecited by 102
  5. 2024A Survey on Data Selection for LLM Instruction Tuningcited by 76
  6. 2025From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agentscited by 49
  7. 2025The Best Instruction-Tuning Data are Those That Fitcited by 48
  8. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  9. 2024Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMscited by 31
  10. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  11. 2024What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspectivecited by 29
  12. 2025ATLaS: Agent Tuning via Learning Critical Stepscited by 29
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  1. 2024Nudging: Inference-time Alignment of LLMs via Guided Decodingcited by 22
  2. 2025The Rise of Small Language Models in Healthcare: A Comprehensive Surveycited by 18
  3. 2024Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?cited by 16
  4. 2025The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authorscited by 16
  5. 2025Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Modelcited by 15
  6. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 14
  7. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challengescited by 14
  8. 2025Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learningcited by 12
  9. 2024API-guided Dataset Synthesis to Finetune Large Code Modelscited by 11
  10. 2024TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Usecited by 11
  11. 2025GoRA: Gradient-driven Adaptive Low Rank Adaptationcited by 11
  12. 2024Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articlescited by 9
  13. 2024Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Modelscited by 9
  14. 2025Learning to Instruct for Visual Instruction Tuningcited by 8
  15. 2024M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Modelscited by 7
  16. 2025Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levelscited by 7
  17. 2025TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological Counselingcited by 7
  18. 2025Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRAcited by 7
  19. 2024How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignmentcited by 6
  20. 2024ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolationcited by 6
  21. 2025Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Trainingcited by 6
  22. 2025Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMscited by 6
  23. 2025Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scalingcited by 6
  24. 2024Focus On This, Not That! Steering LLMs with Adaptive Feature Specificationcited by 5
  25. 2025From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Modelscited by 5
  26. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Modelscited by 5
  27. 2025Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approachescited by 5
  28. 2026KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?cited by 5
  29. 2024Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?cited by 4
  30. 2025Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learningcited by 4
  31. 2025Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuningcited by 4
  32. 2025On the Entropy Calibration of Language Modelscited by 4
  33. 2025Response-Based Knowledge Distillation for Multilingual Jailbreak Prevention Unwittingly Compromises Safetycited by 4
  34. 2024RAVEL: Rare Concept Generation and Editing via Graph-driven Relational Guidancecited by 3
  35. 2025ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generationcited by 3
  36. 2025Reinforcement Learning Improves Traversal of Parametric Knowledge in LLMscited by 3
  37. 202460 Data Points are Sufficient to Fine-Tune LLMs for Question-Answeringcited by 2
  38. 2025MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 2

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