Exploring the Pragmatic Capabilities of Large Language Models:From Overall Assessment to Irony Analysis
This paper analyzes the performance of large language models(LLMs)in understanding and generating pragmatic expressions,with a particular focus on their ability to handle irony.The findings suggest that the latest LLMs have made significant advancements in pragmatic capabilities.However,when dealing with pragmatic phenomena such as irony and humor,they still rely heavily on literal meanings and thus exhibit limited capacity for complex contextual reasoning.Through a further analysis of GPT-4's performance,this study reveals the relationship between the cognitive motivations of LLMs in recognizing and generating irony and the theoretical frameworks of pragmatics.This study offers suggestions for future developments of both pragmatic theories and LLMs.
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