@inproceedings{bruggemann-hou-2026-team,
    title = {Team {T}{\"u}{LK} at {S}em{E}val-2026 Task 1: Humor Generation with Qwen and Group Relative Policy Optimization},
    author = {Br{\"u}ggemann, Konrad  and
      Hou, Luting},
    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-1.67/",
    pages = "463--474",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper addresses the challenge of computational humor generation proposed in SemEval-2026 Task 1: Humor Generation. Our approach leverages Group Relative Policy Optimization, with an LLM serving as the policy and a custom joke rating model providing a reward signal. We demonstrate that this framework is an effective and computationally efficient approach, reliably producing genuinely funny content that adheres to task constraints."
}