On Sarcasm Detection with OpenAI GPT-based Models

Abstract

Sarcasm is a form of irony that requires readers or listeners to interpret its intended meaning by considering context and social cues. Machine learning classification models have long had difficulty detecting sarcasm due to its social complexity and contradictory nature. This paper explores the applications of the Generative Pretrained Transformer (GPT) models, including GPT-3, InstructGPT, GPT-3.5, and GPT-4, in detecting sarcasm in natural language. It tests fine-tuned and zero-shot models of different sizes and releases. The GPT models were tested on the political and balanced (pol-bal) portion of the popular Self-Annotated Reddit Corpus (SARC 2.0) sarcasm dataset. In the fine-tuning case, the largest fine-tuned GPT-3 model achieves accuracy and $F_1$-score of 0.81, outperforming prior models. In the zero-shot case, one of GPT-4 models yields an accuracy of 0.70 and $F_1$-score of 0.75. Other models score lower. Additionally, a model's performance may improve or deteriorate with each release, highlighting the need to reassess performance after each release.

References in corpus (9)

• Training language models to follow instructions with human feedback
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• A Survey of Large Language Models
• Large Language Models are Zero-Shot Reasoners
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
• A Transformer-based approach to Irony and Sarcasm detection
• A Large Self-Annotated Corpus for Sarcasm
• How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
• Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark