Summary of Humor Mechanics: Advancing Humor Generation with Multistep Reasoning, by Alexey Tikhonov and Pavel Shtykovskiy
Humor Mechanics: Advancing Humor Generation with Multistep Reasoning
by Alexey Tikhonov, Pavel Shtykovskiy
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach to generating one-liner jokes through multi-step reasoning is proposed, aiming to reconstruct the process behind creating humorous one-liners. The researchers developed a working prototype for humor generation and conducted comprehensive experiments with human participants to evaluate their 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. The findings demonstrate that the multi-step reasoning approach consistently improves the quality of generated humor. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a machine that can make funny jokes in several steps. They wanted to understand how people create one-liner jokes and then built a prototype to do it themselves. To see if their machine was good, they showed human participants some jokes made by the machine, compared to ones made by humans, and also used a special AI program called GPT-4. The results showed that the machine can make better jokes than other approaches. |
Keywords
» Artificial intelligence » Gpt » Machine learning » Zero shot
