Preference Learning for AI Alignment: a Causal Perspective

TL;DR

This paper frames reward modeling for AI alignment within a causal perspective, highlighting challenges and proposing causally-inspired methods to improve robustness and generalization in preference learning.

Contribution

It introduces a causal paradigm to reward modeling, identifying key assumptions, failure modes, and potential improvements for aligning language models with human preferences.

Findings

Naive reward models can fail due to causal misidentification.

Causally-inspired approaches can enhance model robustness.

The paper outlines future research directions and intervention strategies.

Abstract

Reward modelling from preference data is a crucial step in aligning large language models (LLMs) with human values, requiring robust generalisation to novel prompt-response pairs. In this work, we propose to frame this problem in a causal paradigm, providing the rich toolbox of causality to identify the persistent challenges, such as causal misidentification, preference heterogeneity, and confounding due to user-specific factors. Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation and contrast them with common data collection practices. We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness. Finally, we outline desiderata for future research and practices, advocating targeted interventions to address inherent limitations of observational data.