Sarcasm Detection

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21 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Honeyed Words, Hidden Daggers: Benchmarking and Training LLMs to See Through Mock Politeness
Oct 19, 2026Caixin Kang, Sitong Gong, Tianyu Yan +3Pragmatic Reasoning in Language Models LLM Evaluation
The University of Tokyo · Alaya Lab
Focus on What You Need: Global-to-Local Dynamic Attention for Multimodal Sarcasm Detection
Oct 13, 2026Dehao Wu, Xingjie ZhuangSarcasm Detection Cross-Modal Attention
Xiamen University
BARRAC: Adaptation of an English Aspect-based Sentiment Analysis Approach for Classification Tasks in Arabic Dialects
Sep 30, 2026Ali Almutairi, Gelareh Mohammadi, Imran Razzak +1Arabic NLP Sentiment Analysis
University of New South Wales, Australia · MBZUAI, UAE
CLASH: Counterfactual Auditing of Lexical and Prosodic Reliance in Spoken Sarcasm Detection
Sep 15, 2026Qiyang Sun, Xudong Li, Yupei Li +4Audio-Language Model Evaluation Counterfactual Evaluation
Imperial College London · New York University · Technical University of Munich +3
When Models Hear What They Expect: Diagnosing Prosodic Heuristics in Multimodal Sarcasm Detection
Aug 31, 2026Yongjian Chen, Pengfei Wei, Yiqun Sun +2Audio-Language Model Evaluation Pragmatic Reasoning in Language Models
Magellan Technology Research Institute (MTRI) · Center for Language and Cognition, University of Groningen, the Netherlands
When AI Rewrites, Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social Text
Aug 15, 2026Shresth ShroffSarcasm Detection Sentiment Analysis
Manipal University Jaipur
PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models
Aug 10, 2026Zhanna Mukhametsharip, Vera Demberg, Varsha SureshVLM Evaluation Pragmatic Reasoning in Language Models
Saarland University, Germany · Max Planck Institute for Informatics, Germany
CHARM: Charge Calibration and Acoustic Rescue for LLM-based Multimodal Sarcasm Detection
Jul 13, 2026Qiyang Sun, Yi Chang, Yupei Li +3Language Model Calibration Multimodal Sentiment Analysis
GLAM – the Group on Language, Audio, & Music, Imperial College London, UK · College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, China · College of Computer Science and Electronic Engineering, Hunan University, China +2
ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity
Jun 4, 2026Prathamjyot Singh, Ashima Sood, Sahil Sharma +1Audio Understanding Speech Prosody
Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India · School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom · School of Computing, Ulster University, Belfast, United Kingdom
Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection
May 28, 2026Martha Teiko Teye, Yaw Marfo Missah, Emmanuel Ahene +2Sentiment Analysis Emotion Recognition in Conversations
Cluster of Excellence, University of Stuttgart, 70174 Stuttgart, German · Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana · Institute for Ethics in Artificial Intelligence, Technical University of Munich, 80333 Münich, Germany
ProCrit: Self-Elicited Multi-Perspective Reasoning with Critic-Guided Revision for Multimodal Sarcasm Detection
May 20, 2026Yingjia Xu, Jiulong Wu, Bowen Zhang +3LLM Self-Correction Multimodal CoT Reasoning
School of Computer Science and Technology, Soochow University, Suzhou, China · Baidu Inc., Beijing, China · Zhipu AI, Beijing, China
PC-MNet: Dual-Level Congruity Modeling for Multimodal Sarcasm Detection via Polarity-Modulated Attention
May 4, 2026Maoheng Li, Ling Zhou, Xiaohua Huang +3Cross-Modal Attention Multimodal Fusion
School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR 999078, China · Oulu School, Nanjing Institute of Technology, Nanjing 210096, China · School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR 999078, China, and also with the Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute, Zhuhai 519099, China +2
GRASP: Grounded CoT Reasoning with Dual-Stage Optimization for Multimodal Sarcasm Target Identification
Apr 10, 2026Faxian Wan, Xiaocui Yang, Yifan Cao +3Sarcasm Detection Multimodal CoT Reasoning
School of Computer Science and Engineering, Northeastern University, Shenyang, China
A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection
Apr 9, 2026Wenxian Wang, Xiaohu Luo, Junfeng Hao +4Sarcasm Detection Data Augmentation
Wenxian Wang and Xingshu Chen are with the Key Laboratory of Data Protection and Intelligent Management, the Cyber Science Research Institute, Sichuan University, Chengdu 610207, China · Xiaohu Luo, Junfeng Hao and Haizhou Wang are with the School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China · Xiaoming Gu is with the State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China +1
CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
Mar 23, 2026Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang +2Sarcasm Detection Multimodal Model Evaluation
National Key Laboratory for Multimedia Information Processing, Peking University · School of Computer Science, Peking University · School of Software and Microelectronics, Peking University
SarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning
Mar 5, 2026Zhu Li, Yongjian Chen, Huiyuan Lai +3Sarcasm Detection Multimodal Reasoning
Speech Technology Lab, University of Groningen, The Netherlands · Center for Language and Cognition, University of Groningen, The Netherlands
Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text
Feb 25, 2026Bitan Majumder, Anirban SenSarcasm Detection Small Language Models
Pondicherry University · Ashoka University
MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
Feb 25, 2026Kazi Samin Yasar Alam, Md Tanbir Chowdhury, Tamim Ahmed +2Text Classification Sarcasm Detection
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh · Department of Computer Science, University of Illinois Chicago, Chicago, Illinois, United States of America
Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language
Feb 21, 2026Toheeb Aduramomi Jimoh, Tabea De Wille, Nikola S. NikolovSarcasm Detection Low-Resource Language Processing
Department of Computer Science and Information Systems, University of Limerick, Castletroy, V94 T9PX, Limerick, Ireland
Evaluating Multimodal Large Language Models on Spoken Sarcasm Detection
Sep 18, 2025Zhu Li, Xiyuan Gao, Yuqing Zhang +2Sarcasm Detection Multimodal Model Evaluation
University of Groningen, The Netherlands
Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques
May 31, 2025Raina Gao, Alyssa Jeong, Lang Xiong +4LLM Evaluation Sarcasm Detection
Algoverse AI Research

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Honeyed Words, Hidden Daggers: Benchmarking and Training LLMs to See Through Mock Politeness

Caixin Kang, Sitong Gong, Tianyu Yan, Kaipeng Zhang+2

The University of Tokyo · Alaya Lab

Pragmatic Reasoning in Language Models LLM Evaluation Reinforcement Learning with Verifiable Rewards Sarcasm Detection

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The Chinese idiom "kou mi fu jian" ("honeyed words, hidden daggers") names speech that is polite in form but hostile in function. We present ZhenhuanBench, a 1,592-item benchmark built from expert commentary on Empresses in the Palace, covering speaker intent, emotion-language mismatch, and per-listener uptake. Text-only evaluation covers 34 LLMs. GPT-6 Astra leads the leaderboard at 73.48% overall, while its listener-reaction score is 58.37%. The annotations also supply supervised traces and string-verifiable training rewards. With 338 gold traces, an 8B model outperforms teacher distillation on identical items. Subsequent RL saturates, and chance correction reduces the estimated recoverable performance gap by a factor of 7.5. Our CPR-GRPO loop (Compress-Probe-Replenish) uses this estimate to weight gold supervision, reaching 67.0 on public multiple-choice tasks and 74.5 under the training-matched hidden protocol. Without target-task training, CPR outperforms Base, SFT, and GRPO across six Chinese and English pragmatic and sarcasm evaluations. On three external datasets, training with CPR yields higher mean Macro-F1 than SFT and GRPO.