Text Hallucination

Text hallucination, the generation of factually incorrect or nonsensical information by large language models (LLMs) and related multimodal models, is a significant obstacle to their reliable deployment. Current research focuses on mitigating this issue through methods that either leverage external knowledge sources or refine the model's internal mechanisms to better distinguish between relevant and irrelevant information during text generation. These approaches often involve modifying the decoding process, for example, by selectively weighting input features or incorporating information-theoretic measures to reduce reliance on spurious correlations in training data. Addressing text hallucination is crucial for improving the trustworthiness and practical applicability of these powerful models across various domains.