Haonan Sheng

Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation

Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications. Some works on LLM hallucination mitigation use the model’s internal signals to contrast different output during inference stage. However, these works often focus on simple f