SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
<|reference_start|>SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding: In the era of large language models (LLMs), the task of ``System I''~-~the fast, unconscious, and intuitive tasks, e.g., sentiment analysis, text classification, etc., have been argued to be successfully solved. However, sarcasm, as a subtle linguistic phenomenon, often employs rhetorical devices like hyperbole and figuration to convey true sentiments and intentions, involving a higher level of abstraction than sentiment analysis. There is growing concern that the argument about LLMs' success may not be fully tenable when considering sarcasm understanding. To address this question, we select eleven SOTA LLMs and eight SOTA pre-trained language models (PLMs) and present comprehensive evaluations on six widely used benchmark datasets through different prompting approaches, i.e., zero-shot input/output (IO) prompting, few-shot IO prompting, chain of thought (CoT) prompting. Our results highlight three key findings: (1) current LLMs underperform supervised PLMs based sarcasm detection baselines across six sarcasm benchmarks. This suggests that significant efforts are still required to improve LLMs' understanding of human sarcasm. (2) GPT-4 consistently and significantly outperforms other LLMs across various prompting methods, with an average improvement of 14.0\%$\uparrow$. Claude 3 and ChatGPT demonstrate the next best performance after GPT-4. (3) Few-shot IO prompting method outperforms the other two methods: zero-shot IO and few-shot CoT. The reason is that sarcasm detection, being a holistic, intuitive, and non-rational cognitive process, is argued not to adhere to step-by-step logical reasoning, making CoT less effective in understanding sarcasm compared to its effectiveness in mathematical reasoning tasks.<|reference_end|>
@article{zhang2024sarcasmbench:,
    title={SarcasmBench: Towards Evaluating Large Language Models on Sarcasm
  Understanding},
    author={Yazhou Zhang, Chunwang Zou, Zheng Lian, Prayag Tiwari, Jing Qin},
    journal={arXiv preprint arXiv:2408.11319},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11319},
    primaryClass={cs.CL cs.AI}
}