Policy Prototyping for LLMs: Pluralistic Alignment via Interactive and Collaborative Policymaking
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915199148097536 |
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| author | Feng, K. J. Kevin Cheong, Inyoung Chen, Quan Ze Zhang, Amy X. |
| author_facet | Feng, K. J. Kevin Cheong, Inyoung Chen, Quan Ze Zhang, Amy X. |
| contents | Emerging efforts in AI alignment seek to broaden participation in shaping model behavior by eliciting and integrating collective input into a policy for model finetuning. While pluralistic, these processes are often linear and do not allow participating stakeholders to confirm whether potential outcomes of their contributions are indeed consistent with their intentions. Design prototyping has long advocated for rapid iteration using tight feedback loops of ideation, experimentation, and evaluation to mitigate these issues. We thus propose policy prototyping for LLMs, a new process that draws inspiration from prototyping practices to enable stakeholders to collaboratively and interactively draft LLM policies. Through learnings from a real-world LLM policymaking initiative at an industrial AI lab, we motivate our approach and characterize policy prototyping with four guiding principles. Because policy prototyping emphasizes a contrasting set of priorities compared to previous approaches, we envision our approach to be a valuable addition to the methodological repertoire for collaborative, pluralistic alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08622 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Policy Prototyping for LLMs: Pluralistic Alignment via Interactive and Collaborative Policymaking Feng, K. J. Kevin Cheong, Inyoung Chen, Quan Ze Zhang, Amy X. Human-Computer Interaction Emerging efforts in AI alignment seek to broaden participation in shaping model behavior by eliciting and integrating collective input into a policy for model finetuning. While pluralistic, these processes are often linear and do not allow participating stakeholders to confirm whether potential outcomes of their contributions are indeed consistent with their intentions. Design prototyping has long advocated for rapid iteration using tight feedback loops of ideation, experimentation, and evaluation to mitigate these issues. We thus propose policy prototyping for LLMs, a new process that draws inspiration from prototyping practices to enable stakeholders to collaboratively and interactively draft LLM policies. Through learnings from a real-world LLM policymaking initiative at an industrial AI lab, we motivate our approach and characterize policy prototyping with four guiding principles. Because policy prototyping emphasizes a contrasting set of priorities compared to previous approaches, we envision our approach to be a valuable addition to the methodological repertoire for collaborative, pluralistic alignment. |
| title | Policy Prototyping for LLMs: Pluralistic Alignment via Interactive and Collaborative Policymaking |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.08622 |