HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation

Edward Ajayi; Prasenjit Mitra

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arXiv · 2604.09629

HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation

Edward Ajayi·Prasenjit Mitra

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Abstract

Humor generation poses a significant challenge for Large Language Models (LLMs), because their standard training objective (next-token prediction) inherently conflicts with the surprise and incongruity required for comedy. To bridge this gap, we introduce the Cognitive Synergy Framework, a methodology for generating highquality humor data inspired by psychological theories of humor. Utilizing a Mixtureof-Thought (MoT) approach, we deploy six cognitive personas (e.g., The Absurdist, The Cynic) to synthesize diverse comedic perspectives for a given prompt. This framework produces a theory-grounded dataset, which we use to fine-tune a 7B-parameter student model. We further evaluate two alignment strategies, Direct Preference Optimization (DPO) and an offline group-relative variant O-GRPO, finding that neither improves over SFT. However, our 7B HumorGen model variants significantly outperform larger instruction-tuned baselines and achieve top-tier open-weight performance while remaining competitive with frontier proprietary systems. These results suggest that cognitively driven data curation is more critical than alignment algorithms or model scale for humor generation.

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Edward Ajayi, Prasenjit Mitra. 2026-03-19. HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation. https://arxiv.org/abs/2604.09629

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