# 关注差异：差异引导的可靠性感知融合与自适应LLM提示用于对话情绪识别

> 全文为英语

**Published:** 2026年11月9日
**Author:** Jingwen Wang, Jiao Li, Yuhang Qian, Yuyin Wang, Min Zhang
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/mind-the-divergence-divergence-guided-reliability-aware-fusion-and-adapt

arXiv:2611.08860v1 发布类型：新 摘要：对话中的多模态情绪识别（MERC）整合文本、声学和视觉线索以识别话语级别的情绪。然而，现有方法通常不明确地将多模态处理适应到具体场景中。

arXiv:2611.08860v1 Announce Type: new Abstract: Multimodal emotion recognition in conversations (MERC) integrates textual, acoustic, and visual cues to identify utterance-level emotions. However, existing approaches generally do not explicitly adapt multimodal processing to the degree of inter-modal predictive disagreement. In real conversations, semantic incongruity, concealed emotion, noisy observations, and ambiguity can affect modality-specific emotional evidence differently, while the reliability of individual modalities varies across samples. Rather than identifying each source of inconsistency separately, we model their common consequence for emotion recognition as predictive disagreement among unimodal emotion distributions over the same emotion-label set. Such disagreement makes indiscriminate fusion prone to propagating unreliable or competing evidence. Inspired by the Brunswik Lens Model, we propose MiD-ERC, a divergence-centric framework that quantifies inter-modal predictive disagreement using a normalized divergence score and estimates sample-specific modality reliability. MiD-ERC uses these signals to coordinate divergence-guided cross-modal gating, divergence-modulated reliability weighting, and divergence-conditioned adaptive LLM prompting. The framework is trained end-to-end using only standard emotion annotations, without requiring additional conflict labels, auxiliary data construction, or external retrieval modules. Experiments on IEMOCAP and MELD demonstrate that MiD-ERC achieves state-of-the-art performance, with particularly pronounced improvements on high-divergence samples.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08860). 查看原文.*

**标签：** #Papers with Code #arXiv

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