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

• URL: http://arxiv.org/abs/2304.13835v3
• Date: Thu, 8 Jun 2023 21:45:46 GMT
• Title: Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
• Authors: Jimmy Wei, Kurt Shuster, Arthur Szlam, Jason Weston, Jack Urbanek, Mojtaba Komeili
• Abstract summary: We evaluate the ability of language models to act as one or more characters in multi-party conversations. We find that our new dataset, MultiLIGHT, can help bring significant improvements in the group setting.
• Score: 39.80729604768669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this work, we both collect and evaluate multi-party conversations to study this more general case. We use the LIGHT environment to construct grounded conversations, where each participant has an assigned character to role-play. We thus evaluate the ability of language models to act as one or more characters in such conversations. Models require two skills that pairwise-trained models appear to lack: (1) being able to decide when to talk; (2) producing coherent utterances grounded on multiple characters. We compare models trained on our new dataset to existing pairwise-trained dialogue models, as well as large language models with few-shot prompting. We find that our new dataset, MultiLIGHT, which we will publicly release, can help bring significant improvements in the group setting.

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