@inproceedings{ray-saksainaa-2026-momo,
    title = "{M}o{M}o at {S}em{E}val-2026 Task 9: Inference-Only Prompting vs. Fine-Tuning for Multilingual Polarization Detection",
    author = "Ray, Sushant  and
      Saksainaa, Rakshita",
    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-1.440/",
    pages = "3591--3601",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our submission to SemEval-2026 Task 9 Subtask 1, which focuses on multilingual polarization detection over the POLAR dataset. We compare three adaptation paradigms: fully fine-tuned multilingual encoders, frozen encoders augmented with lightweight residual heads, and inference-only multilingual LLM prompting in zero-shot and few-shot settings. For few-shot prompting, we evaluate both random and similarity-based support example selection. Similarity-based few-shot prompting with a multilingual LLM competes with our fine-tuned encoder baselines while requiring no task-specific training. We further analyze energy usage, stability across prompt selections and per-language behavior to characterize trade-offs between architectural adaptation and prompt-based inference. While our submission uses a fully fine tuned XLM-RoBERTa Large, the results indicate that inference-only prompting can be a competitive and energy-efficient alternative to task-specific fine-tuning in multilingual classification."
}