Hallucination Dataset

Hallucination datasets are crucial for evaluating and mitigating the tendency of large language models (LLMs), particularly multimodal ones, to generate factually incorrect or nonsensical outputs. Current research focuses on creating diverse datasets that capture various types of hallucinations, including those stemming from visual misinterpretations, contextual errors, and inconsistencies between visual and textual information, often employing both real and synthetic data for scalability. These datasets, along with novel evaluation metrics and methods like contrastive decoding and ensemble approaches, are vital for improving the reliability and trustworthiness of LLMs across applications, especially in high-stakes domains like healthcare.