Understanding ironic utterances: A comprehensive examination of ChatGPT-4o

Abstract

Abstract As large language models (LLMs) increasingly permeate various domains of human life, their ability to accurately comprehend and appropriately respond to irony has become a critical challenge. Irony, as a linguistic phenomenon heavily reliant on contextual, cultural, and cognitive factors, places elevated demands on LLMs’ comprehension. Guided by a systematic theoretical framework, this study integrates qualitative and quantitative methods to construct a comprehensive test set. By analyzing the responses of ChatGPT-4o and comparing them with those of human participants, the study examines the model’s accuracy in understanding and responding to different types of irony. The findings reveal that both human participants and ChatGPT-4o achieved perfect accuracy in comprehension tasks involving situational irony, visual irony, and multimodal irony. However, significant difficulties were observed in the comprehension of verbal irony. Furthermore, the study explores key factors affecting ChatGPT-4o′s performance and identifies the primary mechanisms the model tends to rely on when processing ironic utterances. The results indicate that verbal irony, which requires a more sophisticated grasp of emotional tone and complex cognitive abilities, constitutes the primary factor affecting the performance of LLMs in understanding irony. Meanwhile, Grice’s maxim of quality and inferences about interpersonal relationships are the main mechanisms that LLMs tend to rely on when processing ironic utterances. The findings provide empirical support and developmental pathways for enhancing the capacity of AI systems to handle complex pragmatic phenomena. Furthermore, the study offers important insights into the integration of linguistic theory with artificial intelligence, highlighting new directions for future interdisciplinary research.

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