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FaithDial: A Faithful Benchmark for Information-Seeking Dialogue

Paper · 2022 · Nouha Dziri, Ehsan Kamalloo, Sivan Milton et al.

Summary

Knowledge-grounded dialogue systems often hallucinate: they make claims their knowledge source does not support. Much of this comes from the training data. Popular benchmarks such as Wizard of Wikipedia (WoW) contain hallucinated content in most turns, and models reproduce and even amplify it. Rather than only changing the models, the authors fix the data. Crowdworkers on Amazon Mechanical Turk rewrote unfaithful or uncooperative WIZARD turns in WoW. In the rewritten turns, the wizard is a bot that uses only the given Wikipedia snippet, paraphrases it instead of copying, and admits when it does not know something. The result is FaithDial, which human validation found far more faithful than the original.

The paired original and edited responses serve two purposes. They train FaithCritic, a classifier that flags unfaithful utterances, and they train generation models, including a variant with an auxiliary contrastive objective. Models trained on FaithDial are more faithful and more abstractive, and these gains carry over without further training to CMU-DoG and TopicalChat. This makes the resource useful for building more trustworthy assistants.

Key points

  • FaithDial contains about 50K turns across 5.5K conversations. Its splits are 4,094 train, 764 validation and 791 test dialogues, sampled from SEEKER-initiated WoW conversations.
  • Human checks rated 94.4% of FaithDial utterances as faithful, compared with only 20.9% in WoW. Roughly 79.1% of the original WoW responses needed editing.
  • FaithCritic is trained with FaithDial turns as positive examples and WoW turns as negative examples. In zero-shot evaluation it achieves the best results yet reported on BEGIN, outperforming critics trained on other dialogue-coherence datasets by 12.8 F1. It also transfers better to MNLI.
  • The authors benchmarked GPT2, BlenderBot, T5 and control-token models. The best faithfulness and abstractiveness came from intermediate fine-tuning on WoW combined with a contrastive loss.
  • In human evaluation, responses from models trained on FaithDial were judged more interpretable, cooperative and engaging. Annotators were paid about $17–18 per hour.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of EdinburghEdinburgh · United Kingdom255 works · 68,771 citations🏢TencentShenzhen · China66 works · 17,945 citations🎓McGill UniversityMontreal · Canada51 works · 10,476 citations🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada31 works · 13,061 citations🎓University of AlbertaEdmonton · Canada23 works · 2,655 citations🔬Alberta Machine Intelligence InstituteAlberta · Canada2 works · 229 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 685 worksDialogue systems · 401 works

Cite

Cite as Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti and Siva Reddy. 2022. FaithDial: A Faithful Benchmark for Information-Seeking Dialogue. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00529

@article{dziri2022faithdial,
  title = {{FaithDial: A Faithful Benchmark for Information-Seeking Dialogue}},
  author = {Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti and Siva Reddy},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2022},
  doi = {10.1162/tacl\_a\_00529},
  url = {https://doi.org/10.1162/tacl_a_00529}
}

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