Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization

TL;DR

This paper proposes a weak-to-strong framework for language model generalization that improves less capable models through facilitation from stronger models, enhancing alignment and performance without extensive data.

Contribution

It introduces a novel facilitation-based approach enabling weaker models to benefit from stronger models, advancing AI alignment and scalability in multi-agent systems.

Findings

Facilitation improves weaker model performance.

The framework enhances AI alignment and oversight.

Results demonstrate scalable model improvement.

Abstract

The rapid advancement of artificial intelligence systems has brought the challenge of AI alignment to the forefront of research, particularly in complex decision-making and task execution. As these systems surpass human-level performance in sophisticated problems, ensuring their alignment with human values, intentions, and ethical guidelines becomes crucial. Building on previous work in explanation generation for human-agent alignment, we address the more complex dynamics of multi-agent systems and human-AI teams. This paper introduces a novel approach to model alignment through weak-to-strong generalization in the context of language models. We present a framework where a strong model facilitates the improvement of a weaker model, bridging the gap between explanation generation and model alignment. Our method, formalized as a facilitation function, allows for the transfer of capabilities from advanced models to less capable ones without direct access to extensive training data. Our results suggest that this facilitation-based approach not only enhances model performance but also provides insights into the nature of model alignment and the potential for scalable oversight of AI systems.