Policy Prototyping for LLMs: Pluralistic Alignment via Interactive and Collaborative Policymaking

2024-09-13

arXiv.org

AI Summary

To address insufficient stakeholder engagement and the lack of iterative validation in large language model (LLM) alignment, this paper introduces the “policy prototyping” paradigm—a human-centered, collaborative framework for designing LLM behavioral policies. Methodologically, it integrates human-AI co-design, rapid policy sandbox experimentation, multi-round cross-stakeholder workshops, and an empirically grounded iterative evaluation framework—replacing traditional linear alignment with a closed-loop “intention–feedback–revision” cycle. Key contributions include: (1) establishing the first principled foundation for policy prototyping; (2) ensuring fidelity between collective stakeholder input and actual model behavior; and (3) demonstrating in an industrial AI lab that the approach significantly improves policy interpretability, intention fidelity, and cross-group consensus—thereby extending the methodological frontier of collaborative alignment. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

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.

Problem

• Broaden participation in shaping LLM behavior
• Enable interactive and collaborative LLM policymaking
• Ensure outcomes align with stakeholder intentions

Innovation

• Interactive collaborative LLM policy drafting
• Rapid iteration with feedback loops
• Four guiding principles for policy prototyping