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.
INSIDE: llms' internal states retain the power of hallucination detection
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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.