Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)
Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese.To address this gap, we construct Chumor, the first and the largest Chinese humor explanation dataset.Chumor is sourced from Ruo Zhi Ba (RZB, ), a Chinese Reddit-like platform known for sharing intellectually challenging and culturally specific jokes.We test ten LLMs through direct and chain-of-thought prompting, revealing that Chumor poses significant challenges to existing LLMs, with their accuracy slightly above random and far below human.In addition, our analysis highlights that human-annotated humor explanations are significantly better than those generated by GPT-4o and ERNIE 4-turbo .We release Chumor at https://huggingface.
Authors
- Hanchen Xia
- Naihao Deng
- He Wang (ORCID: https://orcid.org/0000-0002-2281-5679)
- Longju Bai (ORCID: https://orcid.org/0000-0002-5031-4724)
- Rada Mihalcea (ORCID: https://orcid.org/0000-0002-0767-6703)
- Jiarui Liu (ORCID: https://orcid.org/0000-0003-4878-6578)
- Yongchuan He
- Zhenjie Sun
- Ruiqi He
- Zenghao Tang
Institutions
- Shanghai Jiao Tong University (CN)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.18653/v1/2025.findings-acl.1122
- Primary Topic
- Shakespeare, Adaptation, and Literary Criticism
- Type
- article
- Field-Weighted Citation Impact
- 0.00