@inproceedings{sun-etal-2026-emomm,
title = "{E}mo{MM}: Benchmarking and Steering {MLLM} for Multimodal Emotion Recognition under Conflict and Missingness",
author = "Sun, Yueru and
Zhang, Yimeng and
Gu, Haoyu and
Chen, Nuo and
She, Dong and
Yao, Xianrong and
Gao, Yang and
Jin, Zhanpeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1018/",
pages = "20351--20371",
ISBN = "979-8-89176-395-1",
abstract = "Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown promise in MER, their internal decision-making mechanisms under modality conflict and missingness remain largely underexplored. In this paper, to systematically investigate these behaviors, we introduce EmoMM, a comprehensive benchmark featuring modality-aligned, conflict, and missing subsets. Through extensive evaluation, we uncover a Video Contribution Collapse (VCC) phenomenon, where MLLM marginalize video evidence due to high token redundancy and modality preferences. To address this, we propose Conflict-aware Head-level Attention Steering (CHASE), a lightweight mechanism that detects modality conflicts and performs inference-time attention steering, effectively mitigating decision bias without retraining the backbone. Experimental results demonstrate that CHASE consistently improves performance across various settings, significantly enhancing the reliability of MLLM in complex affective scenarios."
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<abstract>Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown promise in MER, their internal decision-making mechanisms under modality conflict and missingness remain largely underexplored. In this paper, to systematically investigate these behaviors, we introduce EmoMM, a comprehensive benchmark featuring modality-aligned, conflict, and missing subsets. Through extensive evaluation, we uncover a Video Contribution Collapse (VCC) phenomenon, where MLLM marginalize video evidence due to high token redundancy and modality preferences. To address this, we propose Conflict-aware Head-level Attention Steering (CHASE), a lightweight mechanism that detects modality conflicts and performs inference-time attention steering, effectively mitigating decision bias without retraining the backbone. Experimental results demonstrate that CHASE consistently improves performance across various settings, significantly enhancing the reliability of MLLM in complex affective scenarios.</abstract>
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%0 Conference Proceedings
%T EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness
%A Sun, Yueru
%A Zhang, Yimeng
%A Gu, Haoyu
%A Chen, Nuo
%A She, Dong
%A Yao, Xianrong
%A Gao, Yang
%A Jin, Zhanpeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F sun-etal-2026-emomm
%X Multimodal Emotion Recognition (MER) is critical for interpreting real-world interactions. While Multimodal Large Language Models (MLLM) have shown promise in MER, their internal decision-making mechanisms under modality conflict and missingness remain largely underexplored. In this paper, to systematically investigate these behaviors, we introduce EmoMM, a comprehensive benchmark featuring modality-aligned, conflict, and missing subsets. Through extensive evaluation, we uncover a Video Contribution Collapse (VCC) phenomenon, where MLLM marginalize video evidence due to high token redundancy and modality preferences. To address this, we propose Conflict-aware Head-level Attention Steering (CHASE), a lightweight mechanism that detects modality conflicts and performs inference-time attention steering, effectively mitigating decision bias without retraining the backbone. Experimental results demonstrate that CHASE consistently improves performance across various settings, significantly enhancing the reliability of MLLM in complex affective scenarios.
%U https://aclanthology.org/2026.findings-acl.1018/
%P 20351-20371
Markdown (Informal)
[EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness](https://aclanthology.org/2026.findings-acl.1018/) (Sun et al., Findings 2026)
ACL
- Yueru Sun, Yimeng Zhang, Haoyu Gu, Nuo Chen, Dong She, Xianrong Yao, Yang Gao, and Zhanpeng Jin. 2026. EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20351–20371, San Diego, California, United States. Association for Computational Linguistics.