Mitigating Hallucination

Hallucination, the generation of factually incorrect information by large language and vision-language models (LLMs and VLMs), is a significant challenge hindering their reliable deployment. Current research focuses on mitigating this issue through various methods, including preemptive detection using internal model representations, data augmentation techniques to create counterfactual examples, and contrastive decoding strategies that re-balance attention to visual and textual inputs. Successfully addressing hallucinations is crucial for building trustworthy AI systems across diverse applications, from question answering and text summarization to medical diagnosis and legal research.