TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)
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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown
| Challenge: | Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size. |
| Approach: | They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences. |
| Outcome: | The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size. |
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