Evaluating Large Language Models for Detecting Antisemitism
Authors: name: Jay Patel, name: Hrudayangam Mehta, name: Jeremy Blackburn
Organization: iDRAMA Lab

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.