PolicyPad: Collaborative Prototyping of LLM Policies
K. J. Kevin Feng, Tzu-Sheng Kuo, Quan Ze Jim Chen, Inyoung Cheong, Kenneth Holstein, Amy X. Zhang
摘要
As LLMs gain adoption in high-stakes domains like mental health, domain experts are increasingly consulted to provide input into policies governing their behavior. From an observation of 19 policymaking workshops with 9 experts over 15 weeks, we identified opportunities to better support rapid experimentation, feedback, and iteration for collaborative policy design processes. We present PolicyPad, an interactive system that facilitates the emerging practice of LLM policy prototyping by drawing from established UX prototyping practices, including heuristic evaluation and storyboarding. Using PolicyPad, policy designers can collaborate on drafting a policy in real time while independently testing policy-informed model behavior with usage scenarios. We evaluate PolicyPad through workshops with 8 groups of 22 domain experts in mental health and law, finding that PolicyPad enhanced collaborative dynamics during policy design, enabled tight feedback loops, and led to novel policy contributions. Overall, our work paves expert-informed paths for advancing AI alignment and safety.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper26
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AIMichael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna M. WallachCHI 2020 · 被引用 428 次
- The Alignment Problem from a Deep Learning PerspectiveRichard Ngo, Lawrence Chan, Sören MindermannICLR 2024 · 被引用 296 次
- Red Teaming Language Models with Language ModelsEthan Perez, Saffron Huang, H. Francis Song, Trevor Cai 等EMNLP 2022 · 被引用 239 次
- MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical ReasoningShuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan Ilgen 等NeurIPS 2024 · 被引用 215 次
相关 Paper
- Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to PrinciplesRyan Louie, Ananjan Nandi, William Fang, Cheng Chang 等EMNLP 2024 · 被引用 37 次
- Farsight: Fostering Responsible AI Awareness During AI Application PrototypingZijie J. Wang, Chinmay Kulkarni, Lauren Wilcox, Michael Terry 等CHI 2024 · 被引用 55 次
- Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM BehaviorMinjae Lee, Minsuk KahngCHI 2026 · 被引用 1 次
- PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content CreationMohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. PardosCHI 2025 · 被引用 18 次
- MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' JournalingTaewan Kim, Seolyeong Bae, Hyun Ah Kim, Su-Woo Lee 等CHI 2024 · 被引用 112 次
