YNU-HPCC at SemEval-2026 Task 1: Constraint-Aware In-Context Learning for Multilingual Humor Generation
Year: 2026
Type: conference-paper
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 fewshot 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.
Authors: Xulong Zhang, Jin Wang, Xuejie Zhang
Institution: Yunnan University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Awards: 基于多维度连续情感空间的文本生成技术研究, G5917579187
OpenAlex ID: W7166810646