Language Model Hallucination

Large language model (LLM) hallucination refers to the generation of factually incorrect or nonsensical outputs by LLMs, a significant obstacle to their reliable deployment. Current research focuses on developing better evaluation methods, including knowledge graph-based frameworks and multi-agent debate systems, to detect and quantify hallucinations across various modalities (text, image, speech). This work also explores mitigation strategies, such as fine-tuning with carefully designed loss functions, CLIP-guided decoding for vision-language models, and methods leveraging the models' internal representations to identify and correct errors. Addressing LLM hallucination is crucial for building trustworthy AI systems and ensuring responsible application of these powerful technologies.