An adaptive contrastive learning strategy that uses a model's own sampled response accuracy to create per-region positive and negative training pairs improves LLM truthful rate by up to 6.9% over IDK-SFT.
Can AI assistants know what they don't know? In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27
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Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
An adaptive contrastive learning strategy that uses a model's own sampled response accuracy to create per-region positive and negative training pairs improves LLM truthful rate by up to 6.9% over IDK-SFT.