Fine Grained Hallucination

Fine-grained hallucination in large language models (LLMs) refers to the generation of specific, detailed inaccuracies within model outputs, going beyond simple factual errors. Current research focuses on developing methods for detecting and mitigating these hallucinations, often categorized into types like existence, attribute, and relation errors, across various modalities (text and vision). This involves creating new datasets with fine-grained annotations, designing specialized models for hallucination detection and correction (often incorporating external knowledge retrieval), and employing active learning techniques to improve efficiency. Addressing fine-grained hallucinations is crucial for enhancing the reliability and trustworthiness of LLMs across diverse applications.