Summary of Chumor 2.0: Towards Benchmarking Chinese Humor Understanding, by Ruiqi He et al.
Chumor 2.0: Towards Benchmarking Chinese Humor Understanding
by Ruiqi He, Yushu He, Longju Bai, Jiarui Liu, Zhenjie Sun, Zenghao Tang, He Wang, Hanchen Xia, Rada Mihalcea, Naihao Deng
First submitted to arxiv on: 23 Dec 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 The abstract proposes the creation of Chumor, a Chinese humor explanation dataset designed to address the lack of culturally nuanced humor resources in non-English languages. The dataset is sourced from Ruo Zhi Ba, a popular Chinese platform for sharing jokes, and aims to challenge existing language models (LLMs) to recognize and generate humorous content in Chinese. Through experiments with ten LLMs, the paper finds that Chumor poses significant challenges to these models, which struggle to achieve accuracy levels above random chance or those of human annotators. The proposed dataset is released at this URL, along with project pages, leaderboards, and codebases for further development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chumor is a new Chinese humor explanation dataset created to help language models recognize and understand humorous content in the Chinese language. This dataset is important because it helps us better understand how to make computers laugh and appreciate humor in different cultures. The researchers tested ten language models on this task and found that they struggled to do well, even with human-annotated explanations. This shows that there’s still a lot of work to be done to create computers that can truly understand and generate humor. |
