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

TL;DR

The paper tackles the scalability challenge of aligning increasingly capable language models with human values by proposing a weak-to-strong generalization framework powered by model facilitation. It formalizes the facilitation function $\Phi$, a debate-driven alignment signal $D$, and an alignment mechanism $\Psi$ to transfer capabilities from strong to weak models without extensive retraining, leveraging explanations and debates for transparency. Empirically, it shows substantial gains across NLP tasks, chess puzzles, and reward modeling when combining baseline supervision with auxiliary confidence loss, bootstrapping, and generative finetuning, along with detailed ablations and analyses of imitation versus true generalization and concept saliency. The findings highlight the potential for scalable, interpretable oversight of AI systems and point to future work on more robust debate mechanisms and broader domain applicability to address remaining generalization gaps and limitations.

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.

Figures (5)

• Figure 1: PGR as a function of model size for each task domain. Higher PGR values indicate more effective weak-to-strong learning. The graph shows that PGR generally increases with model size for NLP tasks, while it decreases for larger models in chess tasks, indicating scalability challenges. Reward modeling shows consistently lower PGR, highlighting the need for more advanced methods.
• Figure 2: Accuracy as a function of model size for each task domain. Weak-to-strong generalization improves performance across all tasks, with NLP achieving the highest accuracy. Chess and Reward Modeling show diminishing returns with larger models, highlighting challenges in scaling the approach to more complex tasks and areas for future research
• Figure 3: Student-supervisor agreement across model sizes for different weak-to-strong methods. Declining agreement in bootstrapping and Auxiliary confidence suggests improved generalization, while the baseline method remains stable, indicating limited generalization.
• Figure 4: Concept saliency before and after weak-to-strong learning for key NLP concepts. The figure shows improved linear probe performance, indicating enhanced model representations across all concepts. Larger gains in Sentiment vs. Intent suggest that weak-to-strong learning affects different aspects of language understanding to varying degrees, providing insights for future improvements.
• Figure 5: Distribution of error types from the error analysis. The chart shows that poor quote selection and evidence extraction account for 70% of errors, suggesting improvements in identifying relevant information could enhance weak-to-strong learning. Overfitting errors (20%) are relatively low, indicating the approach generally succeeds in generalizing beyond weak labels, though further improvement is possible

Theorems & Definitions (5)

• Definition 1: Weak Model
• Definition 2: Strong Model
• Definition 3: Facilitation Func.
• Definition 4: Debate Function
• Definition 5: Alignment Function