Summary of Sarcasmbench: Towards Evaluating Large Language Models on Sarcasm Understanding, by Yazhou Zhang et al.
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
by Yazhou Zhang, Chunwang Zou, Zheng Lian, Prayag Tiwari, Jing Qin
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study investigates the capabilities of large language models (LLMs) in detecting sarcasm, a complex linguistic phenomenon that often employs rhetorical devices. The researchers argue that LLMs may not be as successful as previously thought in understanding human sarcasm, which requires higher-level abstraction than sentiment analysis. To evaluate this claim, they selected 11 SOTA LLMs and 8 SOTA pre-trained language models (PLMs) and presented comprehensive evaluations on six widely used benchmark datasets using different prompting approaches. The results highlight three key findings: current LLMs underperform supervised PLMs in sarcasm detection, GPT-4 consistently outperforms other LLMs across various prompting methods, and the few-shot IO prompting method is more effective than zero-shot IO and few-shot CoT. These findings suggest that significant efforts are still required to improve LLMs’ understanding of human sarcasm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are great at doing many things, but can they really understand sarcasm? Sarcasm is a tricky thing because it’s not just about saying the opposite of what you mean. It’s about using special words and phrases to make people laugh or get a reaction. But do LLMs get it? The researchers looked at 11 different types of LLMs and found that they’re actually pretty bad at understanding sarcasm. They did better when given some help, like extra information or guidance on what to look for. This is important because understanding sarcasm is hard for humans too! We use special cues and context to figure out when someone is being sarcastic. Maybe LLMs can learn from us? |
Keywords
» Artificial intelligence » Few shot » Gpt » Prompting » Supervised » Zero shot
