mlsanityData to 7 Oct 2026

Can Pre-trained Language Models Understand Chinese Humor?

arXiv 2407.04105 ↗First posted 4 Jul 2024cs.CL cs.AIcited by 13

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Abstract (arXiv)

Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: em whether PLMs are capable of humor understanding? This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

17 of 48 references.

References · 17

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5 more
  1. 2022A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Predictioncited by 48
  2. 2019Pun Generation with Surprisecited by 37
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  4. 2019"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlinescited by 27
  5. 2022Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanationcited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 3; 2026: 3.

13 papers.

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  1. 2024XMeCap: Meme Caption Generation with Sub-Image Adaptabilitycited by 14
  2. 2024Recent Advancement of Emotion Cognition in Large Language Modelscited by 9
  3. 2024HOTVCOM: Generating Buzzworthy Comments for Videoscited by 7
  4. 2024Chumor 2.0: Towards Benchmarking Chinese Humor Understandingcited by 4
  5. 2025Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detectioncited by 4
  6. 2025CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processingcited by 3
  7. 2026HotComment: A Benchmark for Evaluating Popularity of Online Commentscited by 3
  8. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  9. 2025Pun Unintended: LLMs and the Illusion of Humor Understandingcited by 2
  10. 2026HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillationcited by 1
  11. 2024Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)cited by 0
  12. 2024Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)cited by 0
1 more
  1. 2026Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinesecited by 0