Multi-Faceted Evaluation and Mitigation of Emotion Hallucinations in MLLMs

📅 2026-09-10
📈 Citations: 0
Influential: 0
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🤖 AI Summary
本文针对多模态大语言模型在情绪理解中产生幻觉的问题,提出EHR评估方法和HMER框架以实现细粒度的情绪幻觉缓解。
📝 Abstract
Multimodal large language models (MLLMs) have shown strong potential in open-ended emotion understanding, yet they often generate emotion hallucinations. Evaluating such hallucinations is particularly challenging for two reasons. First, emotion understanding spans multiple cognitive facets, from multimodal perception to psychological reasoning. Second, emotional interpretations are expressed in free-form language, making existing closed-ended protocols insufficient for evaluation. To address these challenges, we introduce EHR (Emotion Hallucination Rate), an evaluator that quantifies emotion hallucinations across six facets: expression, action, audio, instinct, logic, and conclusion. Using EHR, we reveal that existing mitigation methods often reduce hallucinations in some facets while aggravating them in others, exposing the limitation of coarse-grained correction and the need for facet-aware localization and mitigation. Motivated by this finding, we propose HMER (Hallucination-aware Memory-guided Emotion Reasoning), a training-free framework for emotion hallucination mitigation. HMER maintains a Hallucination Memory that records localized hallucinated claims and enables targeted logit rectification, together with an Anchor Memory that preserves reliable intermediate reasoning states to stabilize subsequent generation. By selectively suppressing unreliable cues while preserving trustworthy reasoning context, HMER enables fine-grained mitigation across diverse hallucination facets. Extensive experiments on 19 MLLMs demonstrate the prevalence of emotion hallucinations and the effectiveness of our framework across diverse model architectures.
Problem

Research questions and friction points this paper is trying to address.

emotion hallucinations
multimodal large language models
open-ended emotion understanding
cognitive facets
free-form language
Innovation

Methods, ideas, or system contributions that make the work stand out.

Emotion Hallucination Rate
Hallucination-aware Memory-guided Emotion Reasoning
facet-aware localization and mitigation
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