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.
arXiv:2605.01017v3 Announce Type: replace Abstract: We introduce Xiaohongshu Social Comparison Reader Elicitation (XHS-SCoRE), a reader-grounded benchmark for detecting whether text-only Xiaohongshu...
The paper introduces CSM-MTBench, a benchmark for evaluating machine translation on Chinese social media text. It addresses two main challenges: limited parallel data due to slang and stylistic nuances, and inadequate evaluation metrics that miss these informal features. The benchmark includes two expert-curated subsets—Fun Posts and Social Snippets—and proposes specialized evaluation methods for each, revealing significant differences among over 20 MT models in handling semantic and stylistic aspects.
APRiL is an open‑ended benchmark designed to evaluate Chinese large language models’ social awareness. It comprises 2,196 evaluation instances across six categories, split into two tasks: Social Scenario Analysis, which tests understanding of social cues, mental states, and conflict structures, and Socially Aware Response, which checks whether models can generate appropriate, natural replies. Experiments on twelve models from six families show a consistent gap between understanding and response quality, indicating that stronger scenario analyses tend to improve response performance.
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
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...
CIBuzzBench is a new benchmark that tests cross‑lingual understanding of Chinese internet buzzwords by providing 3,001 buzzwords with English explanations, equivalents, category labels, and harmfulness annotations. The benchmark defines three tasks—Meaning Explanation, Cross‑lingual Equivalent Matching, and Culturally Grounded Harmfulness Detection—to evaluate how well large language models translate and interpret these culturally nuanced terms. Experiments show that current state‑of‑the‑art LLMs still struggle with fine‑grained non‑literal meanings, robust matching, and accurate harmfulness detection across languages.
arXiv:2607. 13189v1 Announce Type: cross Abstract: We present RAGthoven, our system for SemEval-2026 Task 1 (MWAHAHA), Subtask A (multilingual constrained humor generation in English, Spanish, and Chinese).
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.
arXiv:2606. 01845v1 Announce Type: cross Abstract: Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their comprehension of verbal behavior.
The study investigates how multimodal large language models (MLLMs) use prosodic cues in sarcasm detection. By testing Qwen2.5‑Omni and Qwen3‑Omni on Mandarin Chinese and English across five modality conditions, the authors find that adding audio increases false positives without improving true positives. Acoustic error analysis shows that models rely on a stereotypical prosodic pattern—elevated pitch and irregular pausing—that does not align with genuine sarcasm cues, and manipulating these dimensions alone can raise false positive rates up to 60%. The same effect appears in Gemini 3 Flash Preview, indicating the heuristic is not limited to a single architecture.
Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the interaction of multimodal cues and cultural context, making such content a difficult test case for video-language models.
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.