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LLMs struggle to simulate diverse demographic views on hate speech

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the effectiveness of using persona-conditioned Large Language Models to simulate diverse demographic perspectives for hate speech annotation. The study found that current models do not consistently capture human-like inter-group disagreement, in-group sensitivity, or vicarious prediction of other groups' reactions. However, prompting Llama 3.1 with a vicarious approach showed the most promise in approximating human disagreement patterns. AI

IMPACT LLMs may not reliably replace diverse human annotators for nuanced tasks like hate speech detection.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM capabilities.

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) · Paloma Piot, Javier Parapar ·

    From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

    arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough annotations from multiple demographic groups is costly and difficult to scale. Pe…

  2. arXiv cs.CL TIER_1 English(EN) · Javier Parapar ·

    From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

    Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough annotations from multiple demographic groups is costly and difficult to scale. Persona-conditioned Large Language Models (models …

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