mlsanityData to 14 Oct 2026

Societal Alignment Frameworks Can Improve LLM Alignment

arXiv 2503.00069 ↗First posted 27 Feb 2025cs.CY cs.AI cs.CLcited by 8

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 66 of its 135 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

66 of 135 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Collective Constitutional AI: Aligning a Language Model with Public Input12 Jun
  2. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models24 Apr
  3. 2024Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback16 Apr
  4. 2024What are human values, and how do we align AI to them?27 Mar
  5. 2022Constitutional AI: Harmlessness from AI Feedback15 Dec
  6. 2020Learning to summarize from human feedback2 Sep

Other references · 60

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2017Deep reinforcement learning from human preferencescited by 3,912
  6. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  7. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  8. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  9. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  10. 2016Concrete Problems in AI Safetycited by 1,642
  11. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
  12. 2018Datasheets for Datasetscited by 1,386
38 more
  1. 2018Model Cards for Model Reportingcited by 1,080
  2. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  3. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  4. 2020Artificial Intelligence, Values and Alignmentcited by 448
  5. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  6. 2022Memory-Based Model Editing at Scalecited by 447
  7. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 445
  8. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  9. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
  10. 2018AI safety via debatecited by 400
  11. 2024Alignment faking in large language modelscited by 360
  12. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervisioncited by 318
  13. 2017Inverse Reward Designcited by 249
  14. 2023Large Language Model Alignment: A Surveycited by 243
  15. 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
  16. 2022Defining and Characterizing Reward Hackingcited by 203
  17. 2021Visually Grounded Reasoning across Languages and Culturescited by 189
  18. 2021Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasetscited by 181
  19. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 167
  20. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 129
  21. 2023Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimizationcited by 124
  22. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
  23. 2024To Believe or Not to Believe Your LLMcited by 120
  24. 2019Optimal Policies Tend to Seek Powercited by 119
  25. 2024The Ethics of Advanced AI Assistantscited by 107
  26. 2024Benchmarking Vision Language Models for Cultural Understandingcited by 101
  27. 2022The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systemscited by 93
  28. 2023Verbosity Bias in Preference Labeling by Large Language Modelscited by 91
  29. 2021Consequences of Misaligned AIcited by 80
  30. 2024The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harmcited by 70
  31. 2023Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMscited by 64
  32. 2023Group Preference Optimization: Few-Shot Alignment of Large Language Modelscited by 63
  33. 2018Incomplete Contracting and AI Alignmentcited by 62
  34. 2019Metric Learning for Individual Fairnesscited by 52
  35. 2024On Targeted Manipulation and Deception when Optimizing LLMs for User Feedbackcited by 47
  36. 2019Conservative Agency via Attainable Utility Preservationcited by 35
  37. 2024CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledgecited by 28
  38. 2023Probing the Moral Development of Large Language Models through Defining Issues Testcited by 25

50 most cited of 60 All 60 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation
  2. 2025Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs
  3. 2025Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens
  4. 2023From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
  5. 2023The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
  6. 2025Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
  7. 2025An Evaluation of Cultural Value Alignment in LLM
  8. 2025'Too much alignment; not enough culture': Re-balancing cultural alignment practices in LLMs