Humor Generation – Text-based Humor Generation (English)

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, short

Summary

A system for SemEval-2026 Task 1 Subtask A demonstrates constrained text-based humor generation in English. Developed by Hemeshkumar Parthiban and Priyadharsini R, this approach uses structured prompt engineering with a GPT-4–class large language model in a zero-shot setting, avoiding task-specific fine-tuning. Inputs, consisting of either mandatory word pairs or a news headline, are embedded into a fixed instruction template. The system enforces strict stylistic and structural constraints, ensuring single-sentence outputs of 8–12 words with a dry, deadpan tone and subtle expectation shifts, while avoiding exaggerated punchlines or unsafe content. Experimental results indicate that structured prompting significantly improves stylistic alignment compared to unconstrained generation, proving controlled humor generation is achievable through prompt design without additional training.

Key takeaway

For prompt engineers developing constrained text generation systems, this research highlights the efficacy of structured prompt engineering. You can achieve highly controlled outputs, such as specific sentence lengths and tones, using a zero-shot GPT-4 class model without extensive fine-tuning. Consider implementing deterministic decoding and automatic validation steps to ensure replicability and compliance with strict output requirements, significantly improving stylistic alignment and safety.

Key insights

Controlled humor generation is achievable via structured prompt engineering with large language models.

Principles

• Structured prompting improves stylistic alignment in text generation.
• Zero-shot LLM humor generation is feasible without fine-tuning.

Method

Embed inputs (word pairs/headlines) into a fixed instruction template for a GPT-4 class LLM, enforcing stylistic and structural constraints, using deterministic decoding.

In practice

• Design fixed instruction templates for constrained text generation.
• Enforce output length and tone via prompt constraints.

Topics

• Humor Generation
• Prompt Engineering
• Large Language Models
• Zero-shot Learning
• Text Generation
• SemEval 2026

Best for: AI Engineer, Research Scientist, AI Scientist, NLP Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.