We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to test Large Language Models' (LLMs) ability to identify the specific spans of text that express emotion. Unlike traditional emotion recognition tasks that assign a single label to an entire sentence, SEER targets the underexplored task of emotion evidence detection: pinpointing which exact phrases convey emotion. This span-level approach is crucial for applications like empathetic dialogue and clinical support, which need to know how emotion is expressed, not just what the emotion is. SEER includes two tasks: identifying emotion evidence within a single sentence, and identifying evidence across a short passage of five consecutive sentences. It contains new annotations for both emotion and emotion evidence on 1200 real-world sentences. We evaluate 14 open-source LLMs and find that, while some models approach average human performance on single-sentence inputs, their accuracy degrades in longer passages. Our error analysis reveals key failure modes, including overreliance on emotion keywords and false positives in neutral text.
Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation
College of Computer Science and Engineering（计算机科学与工程学院）；Shandong University of Science and Technology（山东科技大学）；Guangxi Key Lab of Human-machine Interaction and Intelligent Decision（广西人机交互与智能决策重点实验室）；Nanning Normal University（南宁师范大学）
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence（广东人工智能与数字经济实验室）；Auckland University of Technology（奥克兰理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；SKL-IOTSC, CIS, University of Macau（澳门科学技术大学SKL-IOTSC、CIS）
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding
Instituto Politécnico Nacional（墨西哥国立理工学院）；Haverford College（哈弗福德学院）；Wollo University（沃尔洛大学）；Pennsylvania State University（宾夕法尼亚州立大学）；University of Hamburg（汉堡大学）
CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning
Department of Informatics（信息学院）；King’s College London（伦敦国王学院）；Institute of Psychology（心理学研究所）；University of Tartu（塔尔图大学）；Department of Psychology（心理学系）；Stanford University（斯坦福大学）；The Alan Turing Institute（艾伦·图灵研究所）
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Institute of Software, CAS（中国科学院软件研究所）；Microsoft（微软）；William&Mary（威廉与玛丽学院）；Department of Psychology, Beijing Normal University（北京师范大学心理学系）；HKUST（香港科技大学）
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
Ubiquitous Knowledge Processing Lab (UKP Lab) Department of Computer Science and Hessian Center for AI (hessian.AI) Technische Universität Darmstadt（通用知识处理实验室（UKP实验室）计算机科学系和海斯曼人工智能中心（hessian.AI）德意志联邦人家电大学）；Department of Psychology, Bar-Ilan University（心理学系，巴伊兰大学）
National Centre for Text Mining（国家文本挖掘中心）；The University of Manchester（曼彻斯特大学）
Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overlook potential ambiguities, in which multiple emotions overlap. We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence. Further…
Indian Institute of Technology Kanpur (IIT-K)（印度理工学院坎普尔分校（IIT-K））
Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment…
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
The Chinese University of Hong Kong（香港中文大学）；Tongyi Lab（通义实验室）；SZTU（深圳技术大学）；Peking University（北京大学）；Tongji University（同济大学）；CASIA（中国科学院自动化所）；Tencent（腾讯）；UCLA（加州大学洛杉矶分校）；Westlake University（西湖大学）；NTU（国立大学）
Emotion cause analysis such as emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE) have gradually attracted the attention of many researchers. However, there are still two shortcomings in the existing research: 1) In most cases, emotion expression and cause are not the whole clause, but the span in the clause, so extracting the clause-pair rather than the span-pair greatly limits its applications in real-world scenarios; 2) It is not enough to extract the emotion expression clause without identifying the emotion categories, the presence of emotion clause does not necessaril…
Sequential Trajectories and Simultaneous Blending: Multi-Emotion Modeling for Instruction-Following TTS
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)（中国科学院计算技术研究所智能信息处理重点实验室）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Cognition-Oriented Emotion Tracing from Causes to Consequences in Real-World Social Scenes
National University of Singapore（新加坡国立大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；University of Oxford（牛津大学）；Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算与智能研究院）
Université d’Avignon（阿维尼翁大学）；Polytechnique Montréal（蒙特利尔理工学院）；Universidad Tecnológica de la Selva（塞尔瓦理工大学）
In this work we present a new small data-set in Computational Creativity (CC) field, the Spanish Literary Sentences for emotions detection corpus (LISSS). We address this corpus of literary sentences in order to evaluate or design algorithms of emotions classification and detection. We have constitute this corpus by manually classifying the sentences in a set of emotions: Love, Fear, Happiness, Anger and Sadness/Pain. We also present some baseline classification algorithms applied on our corpus. The LISSS corpus will be available to the community as a free resource to evaluate or create CC-lik…
BenSyc: Benchmarking Conversational Sycophancy and Human Alignment in LLMs for Bengali Contexts
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；BRAC University（BRAC大学）；University of Toronto（多伦多大学）
As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious interaction, the next-generation AEI need comprehensive human emotion models for training. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. In practice, the strongest models need robust coverage, which means defining the smallest core set of emotions from which all others can be derived. To ac…
EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection
LAION e.V.（LAION研究院）；TU Munich（慕尼黑技术大学）；L3S Research Center（L3S研究中心）；Leibniz University of Hannover（汉诺威莱布尼茨大学）；TU Darmstadt（达姆施塔特技术大学）；Ontocord（Ontocord公司）；DFKI（DFKI研究所）；TIB–Leibniz Information Centre for Science & Technology（TIB-莱布尼茨科学与技术信息中心）
Adobe Systems（奥多比系统公司）；Bloomberg LP（彭博有限合伙企业）；NSIT-Delhi（德里工程学院）；USICT, New Delhi（新德里印度理工学院德里校区信息技术学院）；IIIT-Delhi（德里印度信息技术学院）
In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as Inspirational and Mystery. Each sentence has been annotated into one of the five emotion categories - anger, joy, suspense, sad, and neutral, by three native Hindi speakers with at least ten years of formal education in Hindi. We also discuss challenges…
ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts
Faculty of Information Science and Engineering, University of Information Technology（信息科学与工程学院，信息科技大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
Seoul National University（首尔国立大学）；Automation and Systems Research Institute, Seoul National University（首尔国立大学自动化与系统研究所）
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given sentiments. Our model achieves 5-th/1-th rank in English/Spanish respectively.
School of Information Science and Engineering（信息科学与工程学院）；Yunnan University（云南大学）
It is fairly common to use code-mixing on a social media platform to express opinions and emotions in multilingual societies. The purpose of this task is to detect the sentiment of code-mixed social media text. Code-mixed text poses a great challenge for the traditional NLP system, which currently uses monolingual resources to deal with the problem of multilingual mixing. This task has been solved in the past using lexicon lookup in respective sentiment dictionaries and using a long short-term memory (LSTM) neural network for monolingual resources. In this paper, we (my codalab username is kon…
