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Humor Mechanics: Advancing Humor Generation with Multistep Reasoning
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In this paper, we explore the generation of one-liner jokes through multi-step reasoning. Our work involved reconstructing the process behind creating humorous one-liners and developing a working prototype for humor generation. We conducted comprehensive experiments with human participants to evaluate our approach, comparing it with human-created jokes, zero-shot GPT-4 generated humor, and other baselines. The evaluation focused on the quality of humor produced, using human labeling as a benchmark. Our findings demonstrate that the multi-step reasoning approach consistently improves the quality of generated humor. We present the results and share the datasets used in our experiments, offering insights into enhancing humor generation with artificial intelligence.
Forward citations
Cited by 2 Pith papers
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HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation
Persona-based Mixture-of-Thought data curation lets a 7B student outperform larger models on humor generation, while DPO and O-GRPO add no gain over SFT.
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Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor
Five LLMs frequently misclassify the appropriateness of workplace humor, especially offensive and neutral jokes, on a new 304-item industrial humor dataset.
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