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CLaC system uses LLMs and encoders for political discourse clarity detection

Researchers presented a system for SemEval-2026 Task 6, focusing on detecting clarity and evasion in political discourse. Their approach involved comparing fine-tuned encoders with prompt-based large language models (LLMs). The LLM ensemble achieved strong results, outperforming fine-tuned encoders, particularly on minority classes, and their code and configurations are publicly available. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This research explores LLM capabilities in nuanced text analysis, potentially improving political discourse understanding.

RANK_REASON This is a research paper detailing a system for a specific NLP task at a conference.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Nawar Turk, Lucas Miquet-Westphal, Leila Kosseim ·

    CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse

    arXiv:2605.02170v1 Announce Type: new Abstract: In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews, comparing fine-tuned encoders with prompt-based LLMs. Our L…

  2. arXiv cs.CL TIER_1 · Leila Kosseim ·

    CLaC at SemEval-2026 Task 6: Response Clarity Detection in Political Discourse

    In this paper, we present our system for SemEval-2026 Task 6 (CLARITY) on response clarity and evasion detection in question-answer pairs from U.S. presidential interviews, comparing fine-tuned encoders with prompt-based LLMs. Our LLM ensemble achieves 80 macro-F1 on the 3-class …