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LLMs assist experts in crafting counterspeech against hate speech

Researchers have developed methods using Large Language Models (LLMs) to assist in writing counterspeech against online hate speech and misinformation. The study explored three strategies, including prompting LLMs with fact-checking and NGO guidelines, and a mixed approach combining both. While LLMs generated adequate counterspeech in 40% of cases, expert revisions significantly improved the output's quality and adherence to guidelines. AI

IMPACT Provides a framework for using LLMs to combat online toxicity, potentially improving moderation and reducing polarization.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and dataset.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Genoveffa Martone, Helena Bonaldi, Marco Guerini ·

    Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation

    arXiv:2605.22435v1 Announce Type: new Abstract: Hate speech and misinformation frequently co-occur online, amplifying prejudice and polarization. Given their scale, using Large Language Models (LLMs) to assist expert counterspeech (CS) writing has gained interest, yet prior work …

  2. arXiv cs.CL TIER_1 English(EN) · Marco Guerini ·

    Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation

    Hate speech and misinformation frequently co-occur online, amplifying prejudice and polarization. Given their scale, using Large Language Models (LLMs) to assist expert counterspeech (CS) writing has gained interest, yet prior work has addressed these phenomena separately. We bri…