Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
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.
Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 19 of its 32 references that are in the map. First posted on arXiv 26 Apr 2023.
Builds on
19 of 32 references.
References · 19
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
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- 2020Towards a Human-like Open-Domain Chatbotcited by 467
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- 2022BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagecited by 197
- 2019The Second Conversational Intelligence Challenge (ConvAI2)cited by 178
- 2015The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systemscited by 172
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- 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
- 2022Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completioncited by 102
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- 2022Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agentscited by 62
- 2022The CRINGE Loss: Learning what language not to modelcited by 39
- 2020Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directionscited by 34
- 2015A Survey of Available Corpora for Building Data-Driven Dialogue Systemscited by 33
- 2021Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identitycited by 22
- 2019Generating Interactive Worlds with Textcited by 13
- 2023Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcingcited by 7
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Citing papers per year: 2023: 10; 2024: 20; 2025: 12; 2026: 11.
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