Work

ICT-NLP at SemEval-2026 Task 1: Humor Generation via RAG-based Augmentation and Multi-LLM Internal-External Voting

Year: 2026

Type: conference-paper

Abstract: This paper presents the system we developed for SemEval-2026 Task 1: Humor Generation.The task focuses on developing systems capable of generating genuinely humorous content under various constraints.In this work, we propose using a Retrieval-Augmented Generation approach to preprocess news headlines and obtain summaries of news content.Furthermore, we employ a unified humor generation mode to adapt to the two types of generation constraints.Finally, we conduct an internal-external voting process to produce the final optimal joke output.Our approach achieves competitive performance in this task: it ranks 1st (tied) among all participating teams in the Chinese track of Subtask A.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.247

Authors: Wutao Shen, Liyuan Huang, Jiawei He, Lin Li, Jin Zhang

Institutions: Chinese Academy of Sciences, Institute of Computing Technology

Language: English

Topic: Humor Studies and Applications

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7166844705

FWCI:

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