mlsanityData to 14 Oct 2026

Designing the Future of User Feedback for Generative AI

arXiv 2610.02631 ↗First posted 2 Oct 2026cs.AIcited by 0

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Abstract (arXiv)

Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and features. When implemented effectively, giving such feedback can increase users' engagement with and trust in GenAI systems. Government regulations and industry guidelines call for post-deployment user engagement, but there is little guidance on designing mechanisms that are usable for consumers and provide actionable input for product teams. We conducted a multi-phase study as a collaboration between academic researchers and eBay. Our benchmark evaluation of current industry approaches identified common issues including lack of discoverability, unclear terminology, and inattention to user value. Based on these findings, we developed best-practice recommendations and designed and tested a prototype feedback-collection tool. The tool aimed to provide users with an efficient, flexible, and positive feedback-giving experience, and provide product teams with rich data on performance and potential problems in a usable format.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 89 references that are in the map. First posted on arXiv 2 Oct 2026.

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35 of 89 references.

References · 35

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  3. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  4. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  5. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 759
  6. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 401
  7. 2025Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scalecited by 178
  8. 2024Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learningcited by 155
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  11. 2024Personalized Language Modeling from Personalized Human Feedbackcited by 132
  12. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 126
23 more
  1. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 123
  2. 2024Collective Constitutional AI: Aligning a Language Model with Public Inputcited by 123
  3. 2020Participation is not a Design Fix for Machine Learningcited by 111
  4. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 104
  5. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 87
  6. 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
  7. 2024PersonalLLM: Tailoring LLMs to Individual Preferencescited by 60
  8. 2023The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Valuescited by 54
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  10. 2022Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedbackcited by 36
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  12. 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 30
  13. 2023Toward General Design Principles for Generative AI Applicationscited by 21
  14. 2024D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluationcited by 11
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  18. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 6
  19. 2024Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governancecited by 4
  20. 2024Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generationcited by 3
  21. 2025Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severitycited by 3
  22. 2022Embedding Privacy Into Design Through Software Developers: Challenges & Solutionscited by 1
  23. 2024Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Modelscited by 1

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