Chumor 2.0: Towards Benchmarking Chinese Humor Understanding
This addresses the problem of limited humor understanding benchmarks for Chinese, providing a new dataset and evaluation for researchers, though it is incremental as it extends existing humor benchmarking to a new language.
The authors tackled the lack of resources for culturally nuanced humor in non-English languages by constructing Chumor, the first Chinese humor explanation dataset, which exceeds the size of existing humor datasets and reveals that LLMs perform only slightly above random accuracy, far below human levels.
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 Chinese humor explanation dataset that exceeds the size of existing humor datasets. Chumor is sourced from Ruo Zhi Ba, 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.co/datasets/dnaihao/Chumor, our project page is at https://dnaihao.github.io/Chumor-dataset/, our leaderboard is at https://huggingface.co/spaces/dnaihao/Chumor, and our codebase is at https://github.com/dnaihao/Chumor-dataset.