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The paper proposes a novel scope-conditioned generation framework that integrates structured stereotype characteristics into Large Language Model prompts for effective multilingual counterspeech, validated on a human-curated dataset with significant improvements in factuality and effectiveness.
This paper studies the use of large language models to assist expert counterspeech writing when hate speech and misinformation co-occur, testing knowledge-driven strategies with human evaluation. The mixed strategy combining fact-checkers' and NGOs' guidelines proved most effective.