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

arXiv: https://arxiv.org/abs/2609.04384 · HTML: https://mlsanity.com/paper/2609.04384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 70 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 30 of 30)

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- [How to Get Your LLM to Generate Challenging Problems for Evaluation](https://mlsanity.com/paper/2502.14678/) (2025)
- [From Toxicity in Online Comments to Incivility in American News: Proceed with Caution](https://mlsanity.com/paper/2102.03671/) (2021)
- [Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction](https://mlsanity.com/paper/2501.13125/) (2025)
- [IDGen: Item Discrimination Induced Prompt Generation for LLM Evaluation](https://mlsanity.com/paper/2409.18892/) (2024)
- ["Newspaper Eat" Means "Not Tasty": A Taxonomy and Benchmark for Coded Language in Real-World Chinese Online Reviews](https://mlsanity.com/paper/2601.19932/) (2026)
- [Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language](https://mlsanity.com/paper/2604.15841/) (2026)
- [CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark](https://mlsanity.com/paper/2604.16372/) (2026)
- [Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation](https://mlsanity.com/paper/2602.14770/) (2026)

## Abstract (arXiv)

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.