MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
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Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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Numerous studies have utilized LLMs to augment or synthesize large-scale dialogue benchmarks for assessing responses in everyday social interactions, multi-modal environments, and persona-aligned responses.
“Consequently, numerous studies have started utilizing LLMs to augment or synthesize large-scale dialogue benchmarks for assessing responses in everyday social interactions (Kim et al., 2023), examining responses in multi-modal environment (Feng et al., 2023), and evaluating responses that align with specific persona (Jandaghi et al., 2023).”
From Evaluating Very Long-Term Conversational Memory of LLM Agents (2024) · cites this paper as Feng et al., 2023
The Contributions section describes MMDialog as a million-scale multimodal dialogue dataset with generation and retrieval benchmark tasks, supporting the multimodal-benchmark part of the citation. Section 3, “Dialogue Creation” (page unavailable), says the dialogues were collected from social media, not augmented or synthesized using LLMs. (LLM, conf=0.89)
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- D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants' demographic (e.g., country of residence)? Section 6.3 and they are volunteers. D3. Did you discuss whether and how consent was obtained from people whose data you're using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Section 3, Section 4, Section 6.3 and Ethics Statement D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Section 3, Section 4, Section 6.3 and Ethics Statement D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? Section 3, Section 4, Section 6.3 and Ethics Statement
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Yin, Z., Hui, B., Yang, M., Huang, F., & Li, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448111 · Cited by 2 · see citing works
Xie, P., & Liu, B. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10351-8 · Cited by 1 · see citing works
Emotion-Aware and Efficient Meme Sticker Dialogue Generation
Guo, Z., Huang, J., Li, G., Peng, W., Zhang, X., Qian, Z., & Li, S. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700242 · Cited by 1 · see citing works
Recent Advances on Multi-modal Dialogue Systems: A Survey
Cheng, F., Li, X., Wu, H., Sang, J., & Zhao, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0847-8_3 · Cited by 1 · see citing works
Large Language Models can Share Images, Too!
Lee, Y., Lee, D., Sung, J.-W., Hyeon, J., & Choi, H. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.39 · Cited by 1 · see citing works
Fu, T., Zhao, X., Liu, L., & Yan, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.513
BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation
Yoon, H. S., Yoon, E., Tee, J. T. J., Zhang, K., Heo, Y., Chang, D.-S., & Yoo, C. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_22
Aguirre, M., Méndez, A., Aceta, C., Torralbo, M., del Pozo, A., Fernández, I., Etxalar, A., Torres, M. I., Agirre, J. A., Artetxe, E., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.148
Stimulating conversation-style emergencies of multi-modal LMs
湯浅, 順., Liu, B., Sun, C., Xu, Z., & Wang, B. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103047
Multimodal Contrastive Learning for Dialogue Embeddings with Global and Local Views
Choe, S., Oh, J., & Yang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_13
Ensuring Responses Contain Appropriate Images: Timing Judgment for Multimodal Responses
Yang, H., Zheng, T., Zhao, Y., & Qin, B. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758178
Liang, Y., Wang, X., Xie, T., & Pan, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4384-7_13
Accessible Six-DOF Clay-Printing Platform Using a 3D-Printed Robotic Arm
Čabaravdić, M., & Sarajlić, A. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt.20250181
Engagingness in Open-Domain Dialogue Systems: A Systematic Survey of Datasets, Metrics and Methods
Mitra, E., Ferron, A., & Agrawal, A. (2026) · AI Computer Science and Robotics Technology · doi:10.5772/acrt20250081
MTRP: Diversely Enhancing Multi-Turn Dialogue and Role-Playing Abilities of Large Language Models
Huang, P., Shi, T., Fan, L., Chen, H., Li, Z., Jin, Y., & He, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464035
- Authors
- Feng, J., Sun, Q., Xu, C., Zhao, P., Yang, Y., Tao, C., Zhao, D., & Lin, Q.
- Affiliations
- Peking University (CN)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 7348-7363
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385571466
- Cited by
- 26
- References
- 53
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 8 · 2023: 6
- FWCI
- 4.61
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Social Sciences › Arts and Humanities › Language and Linguistics › Language, Discourse, Communication Strategies
- Keywords
- multi-turn dialogue, open-domain conversation, dialogue dataset, multimodal interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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