@inproceedings{kim-etal-2023-soda,
    title = "{SODA}: Million-scale Dialogue Distillation with Social Commonsense Contextualization",
    author = "Kim, Hyunwoo  and
      Hessel, Jack  and
      Jiang, Liwei  and
      West, Peter  and
      Lu, Ximing  and
      Yu, Youngjae  and
      Zhou, Pei  and
      Bras, Ronan  and
      Alikhani, Malihe  and
      Kim, Gunhee  and
      Sap, Maarten  and
      Choi, Yejin",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.799/",
    doi = "10.18653/v1/2023.emnlp-main.799",
    pages = "12930--12949",
    abstract = "Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present SODA: the first publicly available, million-scale high-quality social dialogue dataset. By contextualizing social commonsense knowledge from a knowledge graph, we are able to distill an exceptionally broad spectrum of social interactions from a large language model. Human evaluation shows that conversations in SODA are more consistent, specific, and (surprisingly) natural than those in prior human-authored datasets. Using SODA, we train COSMO: a generalizable conversation model that is significantly more natural and consistent on unseen datasets than best-performing conversation models (e.g., GODEL, BlenderBot-1, Koala, Vicuna). Experiments reveal COSMO is sometimes even preferred to the original human-written gold responses. Additionally, our results shed light on the distinction between knowledge-enriched conversations and natural social chitchats. We plan to make our data, model, and code public."
}