TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue
  Summarization
Authors: name: Liyan Tang, name: Igor Shalyminov, name: Amy Wing-mei Wong, name: Jon Burnsky, name: Jake W. Vincent, name: Yu'an Yang, name: Siffi Singh, name: Song Feng, name: Hwanjun Song, name: Hang Su, name: Lijia Sun, name: Yi Zhang, name: Saab Mansour, name: Kathleen McKeown

Single document news summarization has seen substantial progress on
faithfulness in recent years, driven by research on the evaluation of factual
consistency, or hallucinations. We ask whether these advances carry over to
other text summarization domains. We propose a new evaluation benchmark on
topic-focused dialogue summarization, generated by LLMs of varying sizes. We
provide binary sentence-level human annotations of the factual consistency of
these summaries along with detailed explanations of factually inconsistent
sentences. Our analysis shows that existing LLMs hallucinate significant
amounts of factual errors in the dialogue domain, regardless of the model's
size. On the other hand, when LLMs, including GPT-4, serve as binary factual
evaluators, they perform poorly and can be outperformed by prevailing
state-of-the-art specialized factuality evaluation metrics. Finally, we
conducted an analysis of hallucination types with a curated error taxonomy. We
find that there are diverse errors and error distributions in model-generated
summaries and that non-LLM based metrics can capture all error types better
than LLM-based evaluators.