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.

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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
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article

Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)

Hanchen Xia, Naihao Deng, He Wang, Longju Bai et al.
Shakespeare, Adaptation, and Literary Criticism
article

Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba)

Hanchen Xia, Naihao Deng, He Wang, Longju Bai, Rada Mihalcea, Jiarui Liu, Yongchuan He, Zhenjie Sun, Ruiqi He, Zenghao Tang
article en

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, ), 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.

Shanghai Jiao Tong University (CN), Carnegie Mellon University (US)
Openalex Percentile: Top 80%
Shakespeare, Adaptation, and Literary Criticism
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