Evaluating Large Language Models for Detecting Antisemitism

• URL: http://arxiv.org/abs/2509.18293v2
• Date: Tue, 04 Nov 2025 20:48:28 GMT
• Title: Evaluating Large Language Models for Detecting Antisemitism
• Authors: Jay Patel, Hrudayangam Mehta, Jeremy Blackburn,
• Abstract summary: We evaluate eight open-source machine-learning models' capability to detect antisemitic content.<n>We design a new CoT-like prompt, Guided-CoT, and find that injecting domain-specific thoughts increases performance and utility.
• Score: 4.368443030353556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting hateful content is a challenging and important problem. Automated tools, like machine-learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs' capability to detect antisemitic content, specifically leveraging in-context definition. We also study how LLMs understand and explain their decisions given a moderation policy as a guideline. First, we explore various prompting techniques and design a new CoT-like prompt, Guided-CoT, and find that injecting domain-specific thoughts increases performance and utility. Guided-CoT handles the in-context policy well, improving performance and utility by reducing refusals across all evaluated models, regardless of decoding configuration, model size, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs' utility, explainability, and reliability. Code and resources available at: https://github.com/idramalab/quantify-llm-explanations

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