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

• URL: http://arxiv.org/abs/2409.07335v1
• Date: Wed, 11 Sep 2024 15:16:25 GMT
• Title: Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization
• Authors: Mehrdad Zakershahrak, Samira Ghodratnama,
• Abstract summary: This paper introduces a novel approach to model alignment through weak-to-strong generalization in the context of language models. Our results suggest that this facilitation-based approach not only enhances model performance but also provides insights into the nature of model alignment.
• Score: 0.6629765271909505
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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