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Weak-To-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

AI Safety Fundamentals: Alignment

CHAPTER

Enhancing Generalization with Auxiliary Confidence Loss in NLP Tasks

This chapter delves into a method of improving generalization in NLP by using an auxiliary confidence loss to fine-tune a strong student model to imitate a weak supervisor's intent while avoiding its mistakes. The approach boosts the strong model's confidence in its predictions, leading to improved performance across tasks with differing model capabilities.

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