@inproceedings{he-etal-2025-chumor,
title = "Chumor 2.0: Towards Better Benchmarking {C}hinese Humor Understanding from (Ruo Zhi Ba)",
author = "He, Ruiqi and
He, Yushu and
Bai, Longju and
Liu, Jiarui and
Sun, Zhenjie and
Tang, Zenghao and
Wang, He and
Xia, Hanchen and
Mihalcea, Rada and
Deng, Naihao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1122/",
doi = "10.18653/v1/2025.findings-acl.1122",
pages = "21799--21818",
ISBN = "979-8-89176-256-5",
abstract = "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, 弱智吧), 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 ERNIE4-turbo. We release **Chumor** at https://huggingface.co/datasets/MichiganNLP/Chumor , our project page is at https://github.com/MichiganNLP/Chumor-2.0 , our leaderboard is at https://huggingface.co/spaces/MichiganNLP/Chumor-leaderboard , and our codebase is at https://github.com/MichiganNLP/Chumor-2.0 ."
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<abstract>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, 弱智吧), 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 ERNIE4-turbo. We release **Chumor** at https://huggingface.co/datasets/MichiganNLP/Chumor , our project page is at https://github.com/MichiganNLP/Chumor-2.0 , our leaderboard is at https://huggingface.co/spaces/MichiganNLP/Chumor-leaderboard , and our codebase is at https://github.com/MichiganNLP/Chumor-2.0 .</abstract>
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%0 Conference Proceedings
%T Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)
%A He, Ruiqi
%A He, Yushu
%A Bai, Longju
%A Liu, Jiarui
%A Sun, Zhenjie
%A Tang, Zenghao
%A Wang, He
%A Xia, Hanchen
%A Mihalcea, Rada
%A Deng, Naihao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F he-etal-2025-chumor
%X 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, 弱智吧), 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 ERNIE4-turbo. We release **Chumor** at https://huggingface.co/datasets/MichiganNLP/Chumor , our project page is at https://github.com/MichiganNLP/Chumor-2.0 , our leaderboard is at https://huggingface.co/spaces/MichiganNLP/Chumor-leaderboard , and our codebase is at https://github.com/MichiganNLP/Chumor-2.0 .
%R 10.18653/v1/2025.findings-acl.1122
%U https://aclanthology.org/2025.findings-acl.1122/
%U https://doi.org/10.18653/v1/2025.findings-acl.1122
%P 21799-21818
Markdown (Informal)
[Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)](https://aclanthology.org/2025.findings-acl.1122/) (He et al., Findings 2025)
ACL
- Ruiqi He, Yushu He, Longju Bai, Jiarui Liu, Zhenjie Sun, Zenghao Tang, He Wang, Hanchen Xia, Rada Mihalcea, and Naihao Deng. 2025. Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba). In Findings of the Association for Computational Linguistics: ACL 2025, pages 21799–21818, Vienna, Austria. Association for Computational Linguistics.