  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, sourced from dialogues in the Chinese
sitcom $\textit{My Own Swordsman}$. It includes 200 carefully handcrafted
questions, all annotated on which Gricean maxims have been violated. We test
eight close-source and open-source LLMs under two tasks: a multiple-choice
question task and an implicature explanation 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 GPT-3.5
and several open-source models, demonstrate a lower accuracy ranging from 20%
to 60% on multiple-choice questions. Human raters were asked to rate the
explanation of the implicatures generated by LLMs on their reasonability, logic
and fluency. While all models generate largely fluent and self-consistent text,
their explanations score low on reasonability except for GPT-4, suggesting that
most LLMs cannot produce satisfactory explanations of the implicatures in the
conversation. Moreover, we find LLMs' performance does not vary significantly
by Gricean maxims, suggesting that LLMs do not seem to process implicatures
derived from different maxims differently. Our data and code are available at
https://github.com/sjtu-compling/llm-pragmatics.
