Hallucination Evaluation

Hallucination evaluation in large language models (LLMs) and vision-language models (VLMs) focuses on developing methods to identify and quantify the generation of inaccurate or fabricated information. Current research emphasizes automated evaluation metrics, often leveraging question-answering or knowledge graph comparisons, to assess factual consistency and faithfulness in model outputs across various modalities (text, images, video) and tasks (summarization, question answering, code generation). These advancements are crucial for improving the reliability and trustworthiness of LLMs and VLMs, particularly in high-stakes applications like healthcare and autonomous systems, where factual accuracy is paramount.