MER 2025: When Affective Computing Meets Large Language Models

MER2025 is the third year of our MER series of challenges. Previously, MER2023 (http://merchallenge.cn/mer2023) focused on multi-label learning, noise robustness, and semi-supervised learning, while MER2024 (https://zeroqiaoba.github.io/MER2024-website) introduced a new track dedicated to open-vocabulary emotion recognition. This year, MER2025 centers on the theme ''When Affective Computing Meets Large Language Models (LLMs)''. We aim to shift the paradigm from traditional categorical frameworks reliant on predefined emotion taxonomies to LLM-driven generative methods, offering innovative solutions for more accurate and reliable emotion understanding. The challenge contains four tracks: MER-SEMI focuses on fixed categorical emotion recognition enhanced by semi-supervised learning; MER-FG explores fine-grained emotions, expanding recognition from basic to nuanced emotional states; MER-DES incorporates multimodal cues (beyond emotion words) into predictions to enhance model interpretability; MER-PR reveals whether emotion prediction results can improve personality recognition performance. For the first three tracks, the baseline code is available at MERTools (https://github.com/zeroQiaoba/MERTools) and datasets can be accessed via Hugging Face (https://huggingface.co/datasets/MERChallenge/MER2025). For the last track, the dataset and baseline code are available on GitHub (https://github.com/cai-cong/MER25_personality).

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Publication Details

Published
2025-10-25
DOI
https://doi.org/10.1145/3746027.3762007
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
4.96
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article

MER 2025: When Affective Computing Meets Large Language Models

Zheng Lian, Haolin Zuo, Kele Xu, Björn W. Schuller et al.
2 citations
Topic Modeling
4.96
article

MER 2025: When Affective Computing Meets Large Language Models

Zheng Lian, Haolin Zuo, Kele Xu, Björn W. Schuller, Zebang Cheng, Xin Liu, Xuefei Liu, Xiong Peng, Guoying Zhao, Rui Liu, Ya Li, Yong Li, Bin Liu, Yazhou Zhang, Jianhua Tao, Erik Cambria, Ziyang Ma, Xie Chen
article en
2 citations

Abstract

MER2025 is the third year of our MER series of challenges. Previously, MER2023 (http://merchallenge.cn/mer2023) focused on multi-label learning, noise robustness, and semi-supervised learning, while MER2024 (https://zeroqiaoba.github.io/MER2024-website) introduced a new track dedicated to open-vocabulary emotion recognition. This year, MER2025 centers on the theme ''When Affective Computing Meets Large Language Models (LLMs)''. We aim to shift the paradigm from traditional categorical frameworks reliant on predefined emotion taxonomies to LLM-driven generative methods, offering innovative solutions for more accurate and reliable emotion understanding. The challenge contains four tracks: MER-SEMI focuses on fixed categorical emotion recognition enhanced by semi-supervised learning; MER-FG explores fine-grained emotions, expanding recognition from basic to nuanced emotional states; MER-DES incorporates multimodal cues (beyond emotion words) into predictions to enhance model interpretability; MER-PR reveals whether emotion prediction results can improve personality recognition performance. For the first three tracks, the baseline code is available at MERTools (https://github.com/zeroQiaoba/MERTools) and datasets can be accessed via Hugging Face (https://huggingface.co/datasets/MERChallenge/MER2025). For the last track, the dataset and baseline code are available on GitHub (https://github.com/cai-cong/MER25_personality).

Beijing University of Posts and Telecommunications (CN), Tianjin Normal University (CN), Tianjin University (CN), National University of Defense Technology (CN), Nanyang Technological University (SG), Shenzhen University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Inner Mongolia University (CN), Shandong Institute of Automation (CN), Institute of Automation (CN), Shenzhen Technology University (CN), Technical University of Munich (DE), Lappeenranta-Lahti University of Technology (FI), Southeast University (CN), University of Oulu (FI), Tsinghua University (CN)
Openalex Percentile: Top 4%
Topic Modeling
4.96
DOIOpen Access PDFOpenAlex
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