mlsanityData to 7 Oct 2026

Building Intelligent User Interfaces for Human-AI Alignment

arXiv 2602.11753 ↗First posted 12 Feb 2026cs.HCcited by 0

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

Abstract (arXiv)

Aligning AI systems with human values fundamentally relies on effective human feedback. While significant research has addressed training algorithms, the role of user interface is often overlooked and only treated as an implementation detail rather than a critical factor of alignment. This paper addresses this gap by introducing a reference model that offers a systematic framework for analyzing where and how user interface contributions can improve human-AI alignment. The structured taxonomy of the reference model is demonstrated through two case studies and a preliminary investigation featuring six user interfaces. This work highlights opportunities to advance alignment through human-computer interaction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 11 of its 22 references that are in the map. First posted on arXiv 12 Feb 2026.

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11 of 22 references.

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Same direction, newest first

  1. 2025DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition24 Jul

Other references · 10

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  5. 2023Interactive AI Alignment: Specification, Process, and Evaluation Alignmentcited by 22
  6. 2023RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedbackcited by 15
  7. 2025Reward Learning from Multiple Feedback Typescited by 9
  8. 2022Time-Efficient Reward Learning via Visually Assisted Cluster Rankingcited by 8
  9. 2025Interactive Groupwise Comparison for Reinforcement Learning from Human Feedbackcited by 4
  10. 2025Human-AI Complementarity: A Goal for Amplified Oversightcited by 3

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  1. 2024Position: Towards Bidirectional Human-AI Alignment
  2. 2025Human-AI Interaction Alignment: Designing, Evaluating, and Evolving Value-Centered AI For Reciprocal Human-AI Futures
  3. 2024Challenges and Future Directions of Data-Centric AI Alignment
  4. 2026Align AI to Dynamic Human-AI Workflows
  5. 2026Co-Constructing Alignment: A Participatory Approach to Situate AI Values ↗
  6. 2024Concept Alignment
  7. 2026SPEAR: Five Principles for Interactive Human-Agent Alignment
  8. 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs