Evaluating Large Language Models for Detecting Antisemitism

Jay Patel, Hrudayangam Mehta, Jeremy Blackburn

Evaluating Large Language Models for Detecting Antisemitism: 2 upvotes on Hugging Face Daily Papers, #35 of 35 papers on 2025-09-26. Day-by-day upvote history.

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 as a policy guideline. We explore various prompting techniques and design a new CoT-like prompt, Guided-CoT. Guided-CoT handles the in-context policy well, increasing performance across all evaluated models, regardless of decoding configuration, model sizes, 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.

Paper page on Hugging Face · arXiv

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