arXiv — NLP / Computation & Language · · 3 min read

You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments

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Computer Science > Computation and Language

arXiv:2609.04384 (cs)
[Submitted on 3 Sep 2026]

Title:You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments

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Abstract:Chinese online comments often convey social meaning through indirect and playful language that is hard to interpret without context. Existing evaluations largely organize items around predefined phenomena or controlled pragmatic categories, leaving open whether models can distinguish plausible readings of what a naturally occurring comment is doing in a particular exchange. We introduce a benchmark for evaluating whether LLMs can recover such situated pragmatic meanings. From more than 200,000 public Chinese social media interaction records, we construct 4,735 human-validated diagnostic items, each pairing a target comment with reconstructed preceding context and plausible misreadings. We evaluate eight LLMs as both question writers and solvers in a cross-writer setting. The task is challenging: the strongest model achieves 81.42% leave-writer-out accuracy. Across all eight models, the mean leave-writer-out accuracy is 68.70% while human accuracy was 90.8%. Case analysis shows that models often recognize broad irony or playfulness while misidentifying the mechanism or interactional move.
Comments: Accepted to the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), Main Conference
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2609.04384 [cs.CL]
  (or arXiv:2609.04384v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.04384
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shiwei Hong [view email]
[v1] Thu, 3 Sep 2026 18:45:54 UTC (207 KB)
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