“Understanding the non-literal meaning of an utterance is critical for large language models(LLMs) to become human-like social communicators. In this work, we introduce SwordsmanImp,the first Chinese multi-turn-dialogue-based dataset aimed at conversational implicature, sourcedfrom dialogues in the Chinese sitcom My Own Swordsman. It includes 200 carefully handcraftedquestions, all annotated on which Gricean maxims have been violated. We test eight close-sourceand open-source LLMs under two tasks: a multiple-choice question task and an implicature ex-planation task. Our results show that GPT-4 attains human-level accuracy (94%) on multiple-choice questions. CausalLM demonstrates a 78.5% accuracy following GPT-4. Other models,including GPT3.5 and several open-source models, demonstrate a lower accuracy ranging from20% to 60% on multiple-choice questions. Human raters were asked to rate the explanation ofthe implicatures generated by LLMs on their reasonability, logic and fluency. While all mod-els generate largely fluent and self-consistent text, their explanations score low on reasonabilityexcept for GPT-4, suggesting that most LLMs cannot produce satisfactory explanations of theimplicatures in the conversation. Moreover, we find LLMs’ performance does not vary signif-icantly by Gricean maxims, suggesting that LLMs do not seem to process implicatures derivedfrom different maxims differently. Our data and code are available at https://github.com/sjtu-compling/llm-pragmatics.”