title: TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue
    Summarization
summary: 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.
paper_authors._id: 65d570a79936544a08a0837a
paper_authors.name: Liyan Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-23T16:36:13.785Z
paper_authors._id: 62c70672e7d825deaae41e5e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62c70672e7d825deaae41e5e/ICCpeBwmQ1NsgWcjG-MEZ.png
paper_authors.fullname: Liyan Tang
paper_authors.type: user
paper_authors.user: lytang
paper_authors._id: 65d570a79936544a08a0837b
paper_authors.name: Igor Shalyminov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T10:43:21.082Z
paper_authors._id: 61fa7a12620023c7e77a096f
paper_authors.avatarUrl: /avatars/dc7e5aded5eb47c937880675a531ab47.svg
paper_authors.fullname: Shalyminov
paper_authors.type: user
paper_authors.user: ishalyminov
paper_authors._id: 65d570a79936544a08a0837c
paper_authors.name: Amy Wing-mei Wong
paper_authors._id: 65d570a79936544a08a0837d
paper_authors.name: Jon Burnsky
paper_authors._id: 65d570a79936544a08a0837e
paper_authors.name: Jake W. Vincent
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T10:40:42.864Z
paper_authors._id: 64a3695aae76a4a3673e91a5
paper_authors.avatarUrl: /avatars/7180f90f37e412ba573990c136110944.svg
paper_authors.fullname: Jake Vincent
paper_authors.type: user
paper_authors.user: jakewvincent
paper_authors._id: 65d570a79936544a08a0837f
paper_authors.name: Yu'an Yang
paper_authors._id: 65d570a79936544a08a08380
paper_authors.name: Siffi Singh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T10:41:14.904Z
paper_authors._id: 63988cf124e61e0d386f22a3
paper_authors.avatarUrl: /avatars/a39d485f8a0ead0e74ad6d5a612e7908.svg
paper_authors.fullname: Siffi Singh
paper_authors.type: user
paper_authors.user: siffi26
paper_authors._id: 65d570a79936544a08a08381
paper_authors.name: Song Feng
paper_authors._id: 65d570a79936544a08a08382
paper_authors.name: Hwanjun Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T10:45:58.484Z
paper_authors._id: 63c9da8d5fdc575773c84816
paper_authors.avatarUrl: /avatars/9357452f7df22c98a3ac6dbb90aa1b14.svg
paper_authors.fullname: Song
paper_authors.type: user
paper_authors.user: Hwanjun
paper_authors._id: 65d570a79936544a08a08383
paper_authors.name: Hang Su
paper_authors._id: 65d570a79936544a08a08384
paper_authors.name: Lijia Sun
paper_authors._id: 65d570a79936544a08a08385
paper_authors.name: Yi Zhang
paper_authors._id: 65d570a79936544a08a08386
paper_authors.name: Saab Mansour
paper_authors._id: 65d570a79936544a08a08387
paper_authors.name: Kathleen McKeown
paper_title: TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue
    Summarization
paper_summary: 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.
paper_ai_summary: Existing large language models generate factually inconsistent topic-focused dialogue summaries and perform poorly as binary factual evaluators, whereas specialized non-LLM metrics capture diverse hallucination types more effectively.
paper_ai_keywords: LLMs topic-focused dialogue summarization factual consistency hallucinations binary sentence-level human annotations GPT-4 binary factual evaluators specialized factuality evaluation metrics error taxonomy