# MemeCMD: An Automatically Generated Chinese Multi-turn Dialogue Dataset with Contextually Retrieved Memes

arXiv: https://arxiv.org/abs/2507.00891 · HTML: https://mlsanity.com/paper/2507.00891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling](https://mlsanity.com/paper/2108.01453/) (2021)
- [Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs](https://mlsanity.com/paper/1812.04718/) (2018)
- [NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation](https://mlsanity.com/paper/2103.02548/) (2021)
- [STICKERCONV: Generating Multimodal Empathetic Responses from Scratch](https://mlsanity.com/paper/2402.01679/) (2024)
- [VoxInstruct: Expressive Human Instruction-to-Speech Generation with Unified Multilingual Codec Language Modelling](https://mlsanity.com/paper/2408.15676/) (2024)
- [Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog](https://mlsanity.com/paper/2003.04679/) (2020)
- [Visual Prompting in LLMs for Enhancing Emotion Recognition](https://mlsanity.com/paper/2410.02244/) (2024)
- [3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement](https://mlsanity.com/paper/2412.18565/) (2024)
- [Multimodal Emoji Prediction](https://mlsanity.com/paper/1803.02392/) (2018)
- [Sticker820K: Empowering Interactive Retrieval with Stickers](https://mlsanity.com/paper/2306.06870/) (2023)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)
- [Reply with Sticker: New Dataset and Model for Sticker Retrieval](https://mlsanity.com/paper/2403.05427/) (2024)

## Abstract (arXiv)

Memes are widely used in online social interactions, providing vivid, intuitive, and often humorous means to express intentions and emotions. Existing dialogue datasets are predominantly limited to either manually annotated or pure-text conversations, lacking the expressiveness and contextual nuance that multimodal interactions provide.To address these challenges, we introduce MemeCMD, an automatically generated Chinese Multi-turn Dialogue dataset with contextually retrieved memes. Our dataset combines a large-scale, MLLM-annotated meme library with dialogues auto-generated by dual agents across diverse scenarios. We introduce a retrieval framework and adaptive threshold to ensure contextually relevant, naturally spaced meme usage. Experiments demonstrate the effectiveness of our approach in generating contextually appropriate and diverse meme-incorporated dialogues, offering a scalable and privacy-preserving resource for advancing multimodal conversational AI.