mlsanityData to 7 Oct 2026

Aligning Large Language Models with Human Preferences through Representation Engineering

arXiv 2312.15997 ↗First posted 26 Dec 2023cs.CLcited by 71

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

Abstract (arXiv)

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation.Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM, and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF's versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.

Key facts. Cited by 71 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 28 of its 49 references that are in the map. First posted on arXiv 26 Dec 2023.

Builds on

28 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  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. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  9. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  10. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  11. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  12. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
15 more
  1. 2020Learning to summarize from human feedbackcited by 2,562
  2. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  3. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  4. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  5. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  7. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 454
  8. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244
  9. 2023The Wisdom of Hindsight Makes Language Models Better Instruction Followerscited by 59
  10. 2018Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learningcited by 57
  11. 2019Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialogcited by 37
  12. 2023Policy Optimization in RLHF: The Impact of Out-of-preference Datacited by 35
  13. 2023A density estimation perspective on learning from pairwise human preferencescited by 23
  14. 2019Budgeted Policy Learning for Task-Oriented Dialogue Systemscited by 13
  15. 2018Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learningcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 19; 2025: 30; 2026: 22.

71 papers.

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  1. 2024A Roadmap to Pluralistic Alignmentcited by 175
  2. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  3. 2024Aligning Large Language Models with Representation Editing: A Control Perspectivecited by 62
  4. 2024Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neuronscited by 47
  5. 2024Advancing Parameter Efficiency in Fine-tuning via Representation Editingcited by 37
  6. 2024Reward-Robust RLHF in LLMscited by 32
  7. 2026Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challengescited by 30
  8. 2025Adaptive Tool Use in Large Language Models with Meta-Cognition Triggercited by 29
  9. 2024Tradeoffs Between Alignment and Helpfulness in Language Models with Steering Methodscited by 26
  10. 2025Advancing Sequential Numerical Prediction in Autoregressive Modelscited by 26
  11. 2024Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?cited by 23
  12. 2024A Survey on Human Preference Learning for Large Language Modelscited by 23
38 more
  1. 2024Towards Uncovering How Large Language Model Works: An Explainability Perspectivecited by 22
  2. 2025Representation Engineering for Large-Language Models: Survey and Research Challengescited by 18
  3. 2026Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generationcited by 18
  4. 2025Learning Dynamics of VLM Finetuningcited by 17
  5. 2024Boost Your Human Image Generation Model via Direct Preference Optimizationcited by 16
  6. 2025A Survey on Progress in LLM Alignment from the Perspective of Reward Designcited by 15
  7. 2025Token-Importance Guided Direct Preference Optimizationcited by 14
  8. 2025RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversalscited by 10
  9. 2026Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Modelscited by 10
  10. 2025From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignmentcited by 9
  11. 2025Too Helpful, Too Harmless, Too Honest or Just Right?cited by 9
  12. 2025Steering Towards Fairness: Mitigating Political Bias in LLMscited by 7
  13. 2024ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimizationcited by 6
  14. 2024Generalizing Reward Modeling for Out-of-Distribution Preference Learningcited by 6
  15. 2025Enhancing Cross-task Transfer of Large Language Models via Activation Steeringcited by 6
  16. 2024Exploring the LLM Journey from Cognition to Expression with Linear Representationscited by 5
  17. 2024Enhancing the Capability and Robustness of Large Language Models through Reinforcement Learning-Driven Query Refinementcited by 5
  18. 2024DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objectscited by 5
  19. 2025Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety Mechanisms Tend to Be Anchored in The Template Regioncited by 5
  20. 2025Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreakingcited by 5
  21. 2025Political Ideology Shifts in Large Language Modelscited by 5
  22. 2025SafeLLM: Unlearning Harmful Outputs from Large Language Models against Jailbreak Attackscited by 4
  23. 2025Monitor-Generate-Verify (MGV): Formalising Metacognitive Theory for Language Model Reasoningcited by 4
  24. 2024Dishonesty in Helpful and Harmless Alignmentcited by 3
  25. 2024Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editingcited by 3
  26. 2024JurEE not Judges: safeguarding llm interactions with small, specialised Encoder Ensemblescited by 3
  27. 2025We Think, Therefore We Align LLMs to Helpful, Harmless and Honest Before They Go Wrongcited by 3
  28. 2025Toward Preference-aligned Large Language Models via Residual-based Model Steeringcited by 3
  29. 2025Before you <think>, monitor: Implementing Flavell's metacognitive framework in LLMscited by 3
  30. 2025Semi-Supervised Preference Optimization with Limited Feedbackcited by 3
  31. 2025Evaluating Steering Techniques using Human Similarity Judgmentscited by 2
  32. 2025Efficient and Stealthy Jailbreak Attacks via Adversarial Prompt Distillation from LLMs to SLMscited by 2
  33. 2025Tree Reward-Aligned Search for TReASURe in Masked Diffusion Language Modelscited by 2
  34. 2025Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editingcited by 2
  35. 2026GENFIG1: Visual Summaries of Scholarly Work as a Challenge for Vision-Language Modelscited by 2
  36. 2026Structuring the Space of Sociotechnical Alignmentcited by 2
  37. 2025Event Segmentation Applications in Large Language Model Enabled Automated Recall Assessmentscited by 1
  38. 2025Amplify Adjacent Token Differences: Enhancing Long Chain-of-Thought Reasoning with Shift-FFNcited by 1

50 most cited of 71 All 71 (JSON)

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  1. 2024Investigating on RLHF methodology
  2. 2025RLTHF: Targeted Human Feedback for LLM Alignment
  3. 2024SEAL: Systematic Error Analysis for Value ALignment
  4. 2025Aligning to What? Limits to RLHF Based Alignment
  5. 2025Beyond RLHF: A Unified Theoretical Framework of Alignment
  6. 2025Doubly Robust Alignment for Large Language Models
  7. 2024Understanding the Learning Dynamics of Alignment with Human Feedback
  8. 2024Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier