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This study evaluates how varying conceptual representations—such as fine-grained taxonomies versus large contexts—affect Large Language Models' ability to detect and reason about antisemitism. The research reveals that while specific taxonomic grounding improves recall, it often reduces precision, and notably, expanding the size of external resources provides no additional quantitative benefit. Furthermore, models struggle significantly with post-Holocaust cases and exhibit systematic flaws like overconfidence and reliance on superficial lexical cues when explaining complex ideological phenomena.
You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism
arXiv:2607.04945v2 Announce Type: replace Abstract: LLMs enable the integration of external conceptual resources at inference time, creating new opportunities for detecting ideologically and historically complex phenomena such as antisemitism. We investigate how different forms of conceptual grounding affect antisemitism detection and explanation behavior across four state-of-the-art LLMs. Using two expert-annotated datasets, we compare definitional, fine-grained taxonomic, example-augmented, and large-context representations of antisemitism. We find that fine-grained taxonomic representations substantially improve recall, while simultaneously reducing precision. Surprisingly, supplying substantially larger conceptual resources yields no additional quantitative benefit. Post-Holocaust antisemitism poses the most persistent challenge across models and configurations. Analysis of explanations further reveals systematic limitations including overproduction of conceptual references, reliance on lexical cues, overconfidence, and difficulties with subtle or justificatory forms of antisemitism. Our findings highlight both the potential and the remaining limitations of conceptually grounded LLMs for antisemitism detection and reasoning.
Abstract:LLMs enable the integration of external conceptual resources at inference time, creating new opportunities for detecting ideologically and historically complex phenomena such as antisemitism. We investigate how different forms of conceptual grounding affect antisemitism detection and explanation behavior across four state-of-the-art LLMs. Using two expert-annotated datasets, we compare definitional, fine-grained taxonomic, example-augmented, and large-context representations of antisemitism. We find that fine-grained taxonomic representations substantially improve recall, while simultaneously reducing precision. Surprisingly, supplying substantially larger conceptual resources yields no additional quantitative benefit. Post-Holocaust antisemitism poses the most persistent challenge across models and configurations. Analysis of explanations further reveals systematic limitations including overproduction of conceptual references, reliance on lexical cues, overconfidence, and difficulties with subtle or justificatory forms of antisemitism. Our findings highlight both the potential and the remaining limitations of conceptually grounded LLMs for antisemitism detection and reasoning.
Computation and Language (cs.CL)
arXiv:2607.04945 [cs.CL]
(or arXiv:2607.04945v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.04945 Focus to learn more arXiv-issued DOI via DataCite
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