"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures
This addresses a key limitation in AI communication for applications like chatbots and virtual assistants, though it is incremental as it builds on existing prompting methods.
The paper tackled the problem of large language models struggling with conversational implicatures by incorporating Grice's Four Maxims via chain-of-thought prompting, resulting in performance surpassing average human levels.
Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances. Although such inferential reasoning is fundamental to human communication, recent research indicates that large language models struggle to comprehend these implicatures as effectively as the average human. This paper demonstrates that by incorporating Grice's Four Maxims into the model through chain-of-thought prompting, we can significantly enhance its performance, surpassing even the average human performance on this task.