SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
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Cited by 21 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 166 papers in the map (data through 7 Oct 2026). Builds on 43 of its 72 references that are in the map. First posted on arXiv 20 Dec 2022.
Builds on
43 of 72 references.
References · 43
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