Team Evaluators at SemEval-2026 Task 6: Instruction-Tuned LLMs for Clarity and Evasion Classification in Political Interviews
Year: 2026
Type: conference-paper
Abstract: This work is part of the SemEval-2026 CLAR-ITY shared task (Task 6), which focuses on detecting clarity and evasion in political questionanswer pairs from interviews and debates.The competition includes two subtasks: claritylevel classification (Clear Reply, Ambiguous, Clear Non-Reply) and evasion-level classification, which identifies one of nine fine-grained evasion techniques.The dataset consists of annotated question-answer pairs with hierarchical labels for both clarity and evasion, enabling comprehensive evaluation of nuanced discourse phenomena.We fine-tune open-source large language models using Low-Rank Adaptation (LoRA) and supervised fine-tuning (SFT), employing structured prompts that jointly encode the question and answer to capture discourse cues.Models are evaluated using Macro F1, the official metric of the shared task.Our system achieves a Macro F1 of 0.83 on Subtask 1 (5th place) and 0.54 on Subtask 2 (9th place), demonstrating that parameter-efficient fine-tuning of LLMs is effective for modeling strategic ambiguity in political discourse.
Authors: Siva Nuthakki, Sanjay Pulagam, Sai Woona
Institution: BML Munjal University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166813013