Datasets › FIB

FIB (Factual Inconsistency Benchmark)

Introduced by Derek Tam et al. in Evaluating the Factual Consistency of Large Language Models Through News Summarization15 Nov 2022 archive 2025-07-28

Factual Inconsistency Benchmark (FIB) is a benchmark that focuses on the task of summarization. Specifically, the benchmark involves comparing the scores an LLM assigns to a factually consistent versus a factual inconsistent summary for an input news article. For factually consistent summaries, human-written reference summaries are used to manually verify as factually consistent.

Source: Evaluating the Factual Consistency of Large Language Models Through Summarization

Image Source: https://arxiv.org/pdf/2211.08412v1.pdf

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 2 papers for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

CC BY 4.0 License

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • FIB

1 variant name, as the archive lists them.

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