Addressing Hallucination in Language Models

Hallucination in large language models (LLMs) refers to their tendency to generate false or non-existent information, undermining reliability. The research introduces ER Bench, a benchmark designed for automatically evaluating hallucinations by utilizing relational databases. These databases not only maintain data integrity through fixed schemas but also allow for better evaluation through functional dependencies, which help identify critical keywords that models should recognize. Thus, the study emphasizes the necessity of not just assessing the accuracy of LLM outputs but also understanding the rationale behind these outputs to enhance trust in their applications.