mlsanityData to 7 Oct 2026

Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis

arXiv 2310.10477 ↗First posted 16 Oct 2023cs.CL cs.AI cs.LGcited by 34

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when LLMs inadvertently generate harmful or toxic content, either unintentionally or because of intentional inducement. Existing alignment methods usually direct LLMs toward the favorable outcomes by utilizing human-annotated, flawless instruction-response pairs. Conversely, this study proposes a novel alignment technique based on mistake analysis, which deliberately exposes LLMs to erroneous content to learn the reasons for mistakes and how to avoid them. In this case, mistakes are repurposed into valuable data for alignment, effectively helping to avoid the production of erroneous responses. Without external models or human annotations, our method leverages a model's intrinsic ability to discern undesirable mistakes and improves the safety of its generated responses. Experimental results reveal that our method outperforms existing alignment approaches in enhancing model safety while maintaining the overall utility.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 40 of its 54 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

40 of 54 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment24 Jul
  2. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 38

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2017Deep reinforcement learning from human preferencescited by 3,846
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  8. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  9. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  10. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  11. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  12. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
26 more
  1. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  2. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignmentcited by 672
  3. 2022Large Language Models Can Self-Improvecited by 656
  4. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  5. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  6. 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 419
  7. 2022Self-critiquing models for assisting human evaluatorscited by 372
  8. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  9. 2018Reward learning from human preferences and demonstrations in Ataricited by 314
  10. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  11. 2019Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialogcited by 283
  12. 2023Challenges and Applications of Large Language Modelscited by 251
  13. 2023MagicDrive: Street View Generation with Diverse 3D Geometry Controlcited by 250
  14. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
  15. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  16. 2019Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attackcited by 176
  17. 2020Recipes for Safety in Open-domain Chatbotscited by 168
  18. 2023Chain of Hindsight Aligns Language Models with Feedbackcited by 147
  19. 2022CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Drivingcited by 100
  20. 2023Safety Assessment of Chinese Large Language Modelscited by 82
  21. 2020How fine can fine-tuning be? Learning efficient language modelscited by 41
  22. 2021MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Drivingcited by 41
  23. 2023Mixed Autoencoder for Self-supervised Visual Representation Learningcited by 38
  24. 2021GPT3-to-plan: Extracting plans from text using GPT-3cited by 26
  25. 2022Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routingcited by 18
  26. 2022Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generationcited by 8

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Cited by year

Citing papers per year: 2023: 5; 2024: 18; 2025: 11.

34 papers.

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  1. 2024Attacks, Defenses and Evaluations for LLM Conversation Safety: A Surveycited by 124
  2. 2024Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformationcited by 114
  3. 2024MLLM-Protector: Ensuring MLLM's Safety without Hurting Performancecited by 111
  4. 2023Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuningcited by 106
  5. 2024Automated Evaluation of Large Vision-Language Models on Self-driving Corner Casescited by 75
  6. 2023CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generationcited by 65
  7. 2024Intention Analysis Makes LLMs A Good Jailbreak Defendercited by 64
  8. 2024ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhancecited by 64
  9. 2023Self-Guard: Empower the LLM to Safeguard Itselfcited by 52
  10. 2024Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulationcited by 46
  11. 2024EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotionscited by 44
  12. 2024DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perceptioncited by 32
22 more
  1. 2024Weak-to-Strong Reasoningcited by 32
  2. 2024SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responsescited by 24
  3. 2024RapGuard: Safeguarding Multimodal Large Language Models via Rationale-aware Defensive Promptingcited by 19
  4. 2023Reasons to Reject? Aligning Language Models with Judgmentscited by 15
  5. 2024Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understandingcited by 12
  6. 2025Recontextualization Mitigates Specification Gaming without Modifying the Specificationcited by 11
  7. 2024YODA: Teacher-Student Progressive Learning for Language Modelscited by 10
  8. 2024Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignmentcited by 10
  9. 2025Alignment of large language models with constrained learningcited by 8
  10. 2024Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Explorationcited by 7
  11. 2024Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Modelscited by 7
  12. 2025Reasoning-Aligned Perception Decoupling for Scalable Multi-modal Reasoningcited by 7
  13. 2025TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioningcited by 6
  14. 2025OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalitiescited by 5
  15. 2025ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Drivingcited by 5
  16. 2024Purple-teaming LLMs with Adversarial Defender Trainingcited by 4
  17. 2025Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventionscited by 3
  18. 2023TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Modelscited by 1
  19. 2025Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuningcited by 1
  20. 2025PSSD: Making Large Language Models Self-denial via Human Psyche Structurecited by 0
  21. 2025Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Datacited by 0
  22. 2025Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimizationcited by 0

Similar, not cited

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  3. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
  4. 2023On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
  5. 2026Are Aligned Large Language Models Still Misaligned?
  6. 2025Rethinking the Evaluation of Alignment Methods: Insights into Diversity, Generalisation, and Safety
  7. 2024Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
  8. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models