MPCEval: A Benchmark for Multi-Party Conversation Generation
arXiv:2603.04969v1 Announce Type: new Abstract: Multi-party conversation generation, such as smart reply and collaborative assistants, is an increasingly important capability of generative AI, yet its evaluation remains a critical bottleneck. Compared to two-party dialogue, multi-party settings introduce distinct challenges, including complex turn-taking, role-dependent speaker behavior, long-range conversational structure, and multiple equally valid continuations. Accordingly, we introduce MPCEval, a task-aware evaluation and benchmarking suite for multi-party conversation generation. MPCEval decomposes generation quality into speaker modeling, content quality, and speaker--content consistency, and explicitly distinguishes local next-turn prediction from global full-conversation generation. It provides novel, quantitative, reference-free, and reproducible metrics that scale across datasets and models. We apply MPCEval to diverse public and real-world datasets and evaluate modern gene
arXiv:2603.04969v1 Announce Type: new Abstract: Multi-party conversation generation, such as smart reply and collaborative assistants, is an increasingly important capability of generative AI, yet its evaluation remains a critical bottleneck. Compared to two-party dialogue, multi-party settings introduce distinct challenges, including complex turn-taking, role-dependent speaker behavior, long-range conversational structure, and multiple equally valid continuations. Accordingly, we introduce MPCEval, a task-aware evaluation and benchmarking suite for multi-party conversation generation. MPCEval decomposes generation quality into speaker modeling, content quality, and speaker--content consistency, and explicitly distinguishes local next-turn prediction from global full-conversation generation. It provides novel, quantitative, reference-free, and reproducible metrics that scale across datasets and models. We apply MPCEval to diverse public and real-world datasets and evaluate modern generation methods alongside human-authored conversations. The results reveal systematic, dimension-specific model characteristics in participation balance, content progression and novelty, and speaker--content consistency, demonstrating that evaluation objectives critically shape model assessment and that single-score evaluation obscures fundamental differences in multi-party conversational behavior. The implementation of MPCEval and the associated evaluation code are publicly available at https://github.com/Owen-Yang-18/MPCEval.
Executive Summary
The article introduces MPCEval, a benchmark for evaluating multi-party conversation generation. MPCEval decomposes generation quality into speaker modeling, content quality, and speaker--content consistency, and provides novel, quantitative, reference-free, and reproducible metrics. The authors apply MPCEval to diverse public and real-world datasets and evaluate modern generation methods alongside human-authored conversations, revealing systematic differences in model characteristics. The results demonstrate that evaluation objectives critically shape model assessment and that single-score evaluation obscures fundamental differences in multi-party conversational behavior. MPCEval's implementation and evaluation code are publicly available, making it a valuable tool for researchers and practitioners in the field of multi-party conversation generation.
Key Points
- ▸ MPCEval is a benchmark for evaluating multi-party conversation generation
- ▸ MPCEval decomposes generation quality into speaker modeling, content quality, and speaker--content consistency
- ▸ The authors apply MPCEval to diverse public and real-world datasets and evaluate modern generation methods alongside human-authored conversations
Merits
Strength
MPCEval provides a comprehensive evaluation framework for multi-party conversation generation, allowing for a more nuanced understanding of model performance and behavior.
Innovative Approach
MPCEval's reference-free and reproducible metrics mark a significant improvement over existing evaluation methods, which often rely on human judgment or reference datasets.
Publicly Available
The implementation of MPCEval and its associated evaluation code are publicly available, making it a valuable resource for researchers and practitioners in the field.
Demerits
Limitation
MPCEval's evaluation framework may not be directly applicable to all types of multi-party conversation generation, such as those with complex turn-taking or role-dependent speaker behavior.
Scalability
The authors note that MPCEval's metrics may not scale across all datasets and models, potentially limiting its effectiveness in certain use cases.
Human Evaluation
While MPCEval provides a comprehensive evaluation framework, it is unclear whether human evaluation is still necessary to provide a complete assessment of model performance.
Expert Commentary
The article presents a significant contribution to the field of multi-party conversation generation, providing a comprehensive evaluation framework and novel metrics that address the challenges of this complex task. The results of MPCEval demonstrate the importance of a nuanced understanding of model performance and behavior in multi-party conversation generation, and highlight the need for more comprehensive evaluation methods in AI and machine learning. While MPCEval has limitations, its publicly available implementation and evaluation code make it a valuable resource for researchers and practitioners in the field.
Recommendations
- ✓ Future research should focus on applying MPCEval to a wider range of datasets and models, as well as exploring its applicability to other types of multi-party conversation generation.
- ✓ The development of more comprehensive evaluation frameworks, such as MPCEval, should be prioritized in the field of AI and machine learning, particularly in applications with complex, multi-party interactions.