@inproceedings{zhang-etal-2026-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 1: Constraint-Aware In-Context Learning for Multilingual Humor Generation",
    author = "Zhang, Xulong  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.96/",
    pages = "664--670",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the system developed by the YNU-HPCC team for SemEval-2026 Task 1 (Humor Generation). The task aims to generate humorous texts from given news headlines or from two unrelated words. The core challenge lies in enabling Large Language Models (LLMs) to understand human humor and align with specific humorous styles. We investigated two approaches: fine-tuning with Proximal Policy Optimization (PPO) and in-context learning with LLMs. We also employed Qwen-Max to evaluate the quality of the generated texts. In the PPO experiments, we constructed a hybrid reward model to align with humor. For our final submission based on LLMs, we used multiple advanced LLMs, along with customized few-shot prompts and a small set of gold samples, to effectively guide the models in generating jokes that resonate with human humor. Experimental results show that our system achieves competitive performance, ranking 4th in the English track, 2nd in the Chinese track, and 2nd in the Spanish track."
}