aiqbench analysis
This paper introduces CUE-Bench, a new benchmark addressing the gap in evaluating large language models on implicit and pragmatic emotion understanding within Chinese discourse. By structuring annotations around nine human-interpretable affective stances rather than just surface sentiment, it demonstrates significant improvements in fine-grained emotion recognition and intent detection. The work advances machine learning research by providing a rigorous framework for assessing how models handle concealed or inverted affective meanings in natural language.
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
arXiv:2608.10810v3 Announce Type: replace-cross Abstract: Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions. Existing emotion benchmarks mainly annotate surface polarity or final emotion categories, while lacking a structured account of how explicit expression, implicit affect, pragmatic intent, and fine grained emotion interact. This limitation makes current evaluations insensitive to cases where affective meaning is concealed, weakened, inverted, or pragmatically reshaped, thereby obscuring model failures in deeper emotion understanding. To address this gap, we introduce CUE Bench, a Chinese Unsaid Emotion benchmark that centers on Affective Stance and covers diverse communicative scenarios. CUE Bench constructs nine human interpretable affective stances from explicit implicit polarity interaction and further provides intent and fine grained emotion annotations for structured affective inference. Experiments show that incorporating Affective Stance improves fine grained emotion recognition by 3.1 percentage points and pragmatic intent detection by 8.1 percentage points over strong baselines.
Abstract:Emotion understanding in discourse requires reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions. Existing emotion benchmarks mainly annotate surface polarity or final emotion categories, while lacking a structured account of how explicit expression, implicit affect, pragmatic intent, and fine grained emotion interact. This limitation makes current evaluations insensitive to cases where affective meaning is concealed, weakened, inverted, or pragmatically reshaped, thereby obscuring model failures in deeper emotion understanding. To address this gap, we introduce CUE Bench, a Chinese Unsaid Emotion benchmark that centers on Affective Stance and covers diverse communicative scenarios. CUE Bench constructs nine human interpretable affective stances from explicit implicit polarity interaction and further provides intent and fine grained emotion annotations for structured affective inference. Experiments show that incorporating Affective Stance improves fine grained emotion recognition by 3.1 percentage points and pragmatic intent detection by 8.1 percentage points over strong baselines.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.10810 [cs.CL]
(or arXiv:2608.10810v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.10810 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 249 words
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