@inproceedings{arampatzis-arampatzis-2026-duth,
    title = "{DUTH} at {S}em{E}val-2026 Task 1: Prompt-Based Zero-Shot Large Language Models for Constrained Multilingual Humor Generation",
    author = "Arampatzis, Georgios  and
      Arampatzis, Avi",
    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-1.83/",
    pages = "579--584",
    ISBN = "979-8-89176-414-9",
    abstract = "Humor generation is a challenging problem fornatural language processing systems due to itssubjectivity, cultural dependence, and relianceon creative language use. These challenges arefurther amplified in constrained multilingualsettings, where models must satisfy explicitlexical or topical requirements while producingshort and humorous outputs.In this paper, we present DUTH{'}s system forSemEval-2026 Task A on constrained multilingual joke generation in English, Spanish, andChinese. Our approach leverages instructiontuned large language models in a zero-shot setting, combining prompt engineering, controlleddecoding, and lightweight post-generation validation to enforce constraint satisfaction andlanguage consistency. We evaluate multiplemodel families and parameter scales, includingQwen and Mistral models. Human evaluationdemonstrates that larger models consistentlyoutperform smaller ones, with Qwen2.5-14BInstruct achieving the strongest overall performance. Error analysis highlights remainingchallenges such as lexical constraint violationsand cross-lingual interference."
}