@inproceedings{
ge2024axioms,
title={Axioms for {AI} Alignment from Human Feedback},
author={Luise Ge and Daniel Halpern and Evi Micha and Ariel D. Procaccia and Itai Shapira and Yevgeniy Vorobeychik and Junlin Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cmBjkpRuvw}
}
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generalizations fail to meet basic axioms. In response, we develop novel rules for learning reward functions with strong axiomatic guarantees. A key innovation from the standpoint of social choice is that our problem has a *linear* structure, which greatly restricts the space of feasible rules and leads to a new paradigm that we call *linear social choice*.
Axioms for AI Alignment from Human Feedback
Luise Ge Daniel Halpern Evi Micha Ariel D. Procaccia Itai Shapira Yevgeniy Vorobeychik Junlin Wu