The Case for Repeatable, Open, and Expert-Grounded Hallucination Benchmarks in Large Language Models

Justin D. Norman, Michael U. Rivera, D. Alex Hughes

Plausible, but inaccurate, tokens in model-generated text are widely believed to be pervasive and problematic for the responsible adoption of language models. Despite this concern,…