SocialArena is a benchmark designed to evaluate the social intelligence of large language models (LLMs) across three dimensions: role‑playing ability, mental state reasoning, and behavioral reasoning. It introduces a role‑playing dialogue paradigm with explicit character thinking to assess these capabilities in realistic human interactions, and a U‑TRIMF framework to synthesize diverse social contexts. The evaluation relies on a human‑annotated arena‑style protocol, supported by a $132.4K investment in recruiting and training annotators who compare model responses and provide preference labels and critiques.
By Zongqi Wang, Rui Wang, Yuchuan Wu, Hua Yang, Bingxue Qiu, Tianle Gu, Bing Zhao, Yujiu Yang, Jieping Ye, Yongbin Li
arXiv:2608. 10810v1 Announce Type: 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.
By Zhenyan Zheng, Yunyao Zhang, Junxi Sheng, Junqing Yu, Zikai Song
arXiv:2601. 16217v2 Announce Type: replace-cross Abstract: Large language models increasingly mediate multilingual professional communication, where useful generation requires adapting to community conventions about which expressions are retained, translated, or mixed.
By Qingyan Yang, Tongxi Wang, Yunsheng Luo
arXiv:2609.08515v1 Announce Type: cross
Abstract: As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical pr...
By Yuemei Xu, Kexin Xu, Jian Zhou, Haoyu Lu, Yequan Wang, Aishan Liu
The paper introduces a benchmark for testing large language models (LLMs) on their ability to infer social pragmatic meanings in indirect and playful Chinese online comments. Using over 200,000 public social media interactions, the authors created 4,735 human-validated diagnostic items that pair a target comment with its preceding context and plausible misreadings. Eight LLMs were evaluated in a cross-writer setting, with the best model achieving 81.42% leave-writer-out accuracy, while human accuracy reached 90.8%. The study finds that models can detect broad irony or playfulness but often misidentify the specific mechanism or interactional move.
By Shiwei Hong, Junjie Ma, Emma Jiren Wang, Ethan Z. Rong, Siying Hu, Haichang Li, Ziying Wang, Zhicong Lu
arXiv:2608.28405v1 Announce Type: new
Abstract: Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common...
By Bryan Chen Zhengyu Tan, Weihua Zheng, Thong T. Doan, Bich Ngoc Doan, Jia Wang Peh, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Zhengyuan Liu, JinYeong Bak, Wafi Shamdi, Soo Kai Chie, Liew Yu Siong, Aina Azyyati Binti Mohamad Rezal, Lew Yan Yan Vanessa, Huadan Wu, Dylan Raharja, Nadya Yuki Wangsajaya, Akane Fukushige, Kazushi Kato, Koji Inoue, Tatsuya Kawahara, Jaehyung Seo, Dongjun Kim, Seungyoon Lee, Zi Haur Pang, Rui Yang Tan, Charibeth Ko Cheng, Maria Regina Justina Estuar, Jann Railey Montalan, Pham Minh Duc, Roy Ka-Wei Lee
arXiv:2510. 08543v2 Announce Type: replace-cross Abstract: As Video Large Language Models (VideoLLMs) are deployed globally, it is important to assess their ability to reason across cultural contexts.
By Nikhil Reddy Varimalla, Yunfei Xu, Meng Fan Wang, Arkadiy Saakyan, Smaranda Muresan
SocialMaze is a new benchmark designed to evaluate large language models on social reasoning tasks that involve deep reasoning, dynamic interaction, and information uncertainty. It comprises six tasks drawn from social deduction games, everyday interactions, and digital communities, and includes automated checks and human validation to ensure data quality. Experiments with twelve LLMs reveal that stronger chain‑of‑thought reasoning improves performance on deeper inference tasks, while uncertainty consistently hurts results; targeted fine‑tuning on curated reasoning traces can markedly enhance structured social‑reasoning abilities.
By Zixiang Xu, Yanbo Wang, Yue Huang, Haomin Zhuang, Yujun Zhou, Jiayi Ye, Sixian Li, Zirui Song, Lang Gao, Chenxi Wang, Zhaorun Chen, Wang Pan, Yue Zhao, Jieyu Zhao, Xiangliang Zhang, Xiuying Chen
arXiv:2609.18156v1 Announce Type: new
Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
By Jianan Wu
arXiv:2609.00491v1 Announce Type: new
Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
By Hangxiao Zhu, Suliu Qin, Zhuoyan Li, Ming Jiang, Yu Zhang, Meng Xia
PIVOTSBench is a benchmark designed to assess multimodal large language models’ ability to reason about fine‑grained interpersonal relationships. It is constructed from Social‑IQ 2.0 and YouTube data and evaluates models on predicting bidirectional relationship dimensions grounded in psychology research. The benchmark also includes auxiliary tasks that test models’ capacity to identify and use critical visual cues, and it examines the impact of visual modalities, social role information, and different prediction settings on model performance.
By Shuxiang Zhang, Yiting Yin, Wenxuan Song, Yuhang Wu, Miao Liu
WorldBench is a new multilingual benchmark that tests large language model agents on culturally grounded everyday workflows, offering 1,600 tasks in seven languages and eight cultures. The benchmark evaluates agents through structured sandbox actions and introduces Constrained Task Success (CTS), a metric that assesses task completion, minimal modification, and other complementary aspects via deterministic and LLM-as-a-Judge evaluations. Experiments show that even leading models achieve only 49.2% CTS, revealing significant gaps in correctness and state preservation across languages and cultures.
By Leonardo Ranaldi, Sherrie Shen, Jushi Kai, Alexandra Birch