mlsanityData to 14 Oct 2026

CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing

arXiv 2503.20417 ↗First posted 26 Mar 2025cs.CLcited by 3

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Abstract (arXiv)

Humor plays a significant role in daily language communication. With the rapid development of large language models (LLMs), natural language processing has made significant strides in understanding and generating various genres of texts. However, most LLMs exhibit poor performance in generating and processing Chinese humor. In this study, we introduce a comprehensive Chinese humor-related dataset, the Chinese Fun Set (CFunSet). This dataset aggregates existing Chinese humor datasets and includes over 20,000 jokes collected from Tieba-JokeBar, a Chinese online platform known for joke sharing. The resulting corpus comprises more than 160,000 entries. Leveraging CFunSet, we developed the Chinese Fun Model (CFunModel), the first large language model designed to handle various Chinese humor-related tasks including Crosstalk Response Selection, Humor Recognition, Joke Generation, etc. Experimental results demonstrate that CFunModel outperforms popular large language models in these tasks. Our CFunSet is available at https://huggingface.co/datasets/ZhenghanYU/CFunSet and CFunModel is available at https://huggingface.co/ZhenghanYU/CFunModel. A demostration video of our work is available at https://youtu.be/MOsISOJ66Ms.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

4 of 12 references.

References · 4

  1. 2024Can Pre-trained Language Models Understand Chinese Humor?cited by 13
  2. 2022Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalkcited by 6
  3. 2024Chumor 2.0: Towards Benchmarking Chinese Humor Understandingcited by 4
  4. 2025Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversationcited by 1

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Citing papers per year: 2025: 1; 2026: 2.

3 papers.