MoMo at SemEval-2026 Task 9: Inference-Only Prompting vs. Fine-Tuning for Multilingual Polarization Detection
Year: 2026
Type: conference-paper
Abstract: We describe our submission to SemEval-2026 Task 9 Subtask 1, which focuses on multilingual polarization detection over the PO-LAR 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.
Authors: Sushant Ray, Rakshita Saksainaa
Institution: Delhi Skill and Entrepreneurship University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166882230