The Future of Open Human Feedback
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910588994584576 |
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| author | Don-Yehiya, Shachar Burtenshaw, Ben Astudillo, Ramon Fernandez Osborne, Cailean Jaiswal, Mimansa Kuo, Tzu-Sheng Zhao, Wenting Shenfeld, Idan Peng, Andi Yurochkin, Mikhail Kasirzadeh, Atoosa Huang, Yangsibo Hashimoto, Tatsunori Jernite, Yacine Vila-Suero, Daniel Abend, Omri Ding, Jennifer Hooker, Sara Kirk, Hannah Rose Choshen, Leshem |
| author_facet | Don-Yehiya, Shachar Burtenshaw, Ben Astudillo, Ramon Fernandez Osborne, Cailean Jaiswal, Mimansa Kuo, Tzu-Sheng Zhao, Wenting Shenfeld, Idan Peng, Andi Yurochkin, Mikhail Kasirzadeh, Atoosa Huang, Yangsibo Hashimoto, Tatsunori Jernite, Yacine Vila-Suero, Daniel Abend, Omri Ding, Jennifer Hooker, Sara Kirk, Hannah Rose Choshen, Leshem |
| contents | Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desirable and safe behaviors. However, this feedback is mostly collected by frontier AI labs and kept behind closed doors. In this work, we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI. We first look for successful practices in 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 center of this ecosystem are mutually beneficial feedback loops, between users and specialized models, incentivizing a diverse stakeholders community of model trainers and feedback providers to support a general open feedback pool. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Future of Open Human Feedback Don-Yehiya, Shachar Burtenshaw, Ben Astudillo, Ramon Fernandez Osborne, Cailean Jaiswal, Mimansa Kuo, Tzu-Sheng Zhao, Wenting Shenfeld, Idan Peng, Andi Yurochkin, Mikhail Kasirzadeh, Atoosa Huang, Yangsibo Hashimoto, Tatsunori Jernite, Yacine Vila-Suero, Daniel Abend, Omri Ding, Jennifer Hooker, Sara Kirk, Hannah Rose Choshen, Leshem Human-Computer Interaction Artificial Intelligence Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desirable and safe behaviors. However, this feedback is mostly collected by frontier AI labs and kept behind closed doors. In this work, we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI. We first look for successful practices in 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 center of this ecosystem are mutually beneficial feedback loops, between users and specialized models, incentivizing a diverse stakeholders community of model trainers and feedback providers to support a general open feedback pool. |
| title | The Future of Open Human Feedback |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16961 |