The future of open human feedback

Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo, Cailean Osborne, Mimansa Jaiswal, Tzu-Sheng Kuo, Wenting Zhao, Idan Shenfeld, Andi Peng, Mikhail Yurochkin, Atoosa Kasirzadeh, Yangsibo Huang, Tatsunori Hashimoto, Yacine Jernite, Daniel Vila-Suero, Omri Abend, Jennifer Ding, Sara Hooker, Hannah Rose Kirk, Leshem Choshen

Abstract: Human feedback on conversations with language models is central to how these systems learn about the world, improve their capabilities and are steered towards desirable and safe behaviours. However, this feedback is mostly collected by frontier artificial intelligence labs and kept behind closed doors. Here we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for artificial intelligence. We first look for successful practices in the peer-production, open-source and citizen-science communities. We then characterize the main challenges for open human feedback. For each, we survey current approaches and offer recommendations. We end by envisioning the components needed to underpin a sustainable and open human feedback ecosystem. In the centre of this ecosystem are mutually beneficial feedback loops, between users and specialized models, incentivizing a diverse stakeholder community of model trainers and feedback providers to support a general open feedback pool. Don-Yehiya et al. explore creating an open ecosystem for human feedback on large language models, drawing from peer-production, open-source and citizen-science practices, and addressing key challenges to establish sustainable feedback loops between users and specialized models.

External IDs:doi:10.1038/s42256-025-01038-2