Does C hat GPT Resemble Humans in Processing Implicatures? (2023)

Abstract: Recent advances in large language models (LLMs) and LLM-driven chatbots, such as ChatGPT, have sparked interest in the extent to which these artificial systems possess human-like linguistic abilities. In this study, we assessed ChatGPT’s pragmatic capabilities by conducting three preregistered experiments focused on its ability to compute pragmatic implicatures. The first experiment tested whether ChatGPT inhibits the computation of generalized conversational implicatures (GCIs) when explicitly required to process the text’s truth-conditional meaning. The second and third experiments examined whether the communicative context affects ChatGPT’s ability to compute scalar implicatures (SIs). Our results showed that ChatGPT did not demonstrate human-like flexibility in switching between pragmatic and semantic processing. Additionally, ChatGPT’s judgments did not exhibit the well-established effect of communicative context on SI rates.

## introduction in recent years, large language models (llms) have achieved unprecedented success in various linguistic tasks, such as disambiguation @xcite , question answering @xcite and translation @xcite the computation of such implicature is believed 141 to follow general principles of conversation and 142 involve reasoning about the possible alternatives 143 that the speaker could have used @xcite the original study of @xcite included a third condition known as the "literal lucy" condition, which was also included in our preregistration. we specified that we would only collect data for this possibility that the window is located elsewhere.