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Current dialogue research primarily studies pairwise (two-party)conversations, and does not address the everyday setting where more than twospeakers converse together. In this work, we both collect and evaluatemulti-party conversations to study this more general case. We use the LIGHTenvironment to construct grounded conversations, where each participant has anassigned character to role-play. We thus evaluate the ability of languagemodels to act as one or more characters in such conversations. Models requiretwo skills that pairwise-trained models appear to lack: (1) being able todecide when to talk; (2) producing coherent utterances grounded on multiplecharacters. We compare models trained on our new dataset to existingpairwise-trained dialogue models, as well as large language models withfew-shot prompting. We find that our new dataset, MultiLIGHT, which we willpublicly release, can help bring significant improvements in the group setting.

Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models