Papers with humor generation
Towards Generation and Recognition of Humorous Texts in Portuguese (2023.eacl-srw)
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| Challenge: | This PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese. |
| Approach: | They propose to combine natural language generation and cognitive processing to generate and recognize verbal humor in Portuguese. |
| Outcome: | The proposed methods aim to generate and recognize humor in Portuguese, an underdeveloped language compared to English. |
Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models (2026.eacl-long)
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| Challenge: | Large language models are increasingly used for creative writing and engagement content, raising safety concerns about their outputs. |
| Approach: | They evaluate how funniness optimization in large language models couples with harmful content by jointly measuring humor, stereotypicality, and toxicity. |
| Outcome: | The proposed model couples humor, stereotypicality, and toxicity with harmful outputs . the results suggest a bias amplification loop between generators and evaluators . |
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk (2023.acl-long)
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| Challenge: | Pre-trained language models have been widely used in NLP, but their social or cultural impact is under-explored. |
| Approach: | They build a dataset consisting of numerous **C**hinese **C*omical **C***rosstalk scripts, which is for a popular Chinese performing art called ‘Xiangsheng’ or ‘’ since 1800s. |
| Outcome: | The proposed approach can generate humor as humans do, but it is still in its infancy. |
Small But Funny: A Feedback-Driven Approach to Humor Distillation (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have been used to transfer knowledge from LLMs to smaller, smaller language models (SLMs). |
| Approach: | They propose to assign a dual role to the LLM as a “teacher” generating data, as well as evaluating the student’s performance. |
| Outcome: | The proposed approach narrows the performance gap between LLMs and larger models by incorporating feedback into the data. |