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#hate-speech

3 curated events
papersSEP 10 04:00 UTC

Study introduces 'Missed-in-Urdu' scores to measure LLM hate speech detection gaps

A new arXiv paper examines how large language models detect hate speech in Urdu, a language with roughly 246 million speakers that the authors say has been largely overlooked in mainstream AI safety evaluation, including nine years of the Workshop on Online Abuse and Harms. The researchers propose 'Missed-in-Urdu' scores to quantify inconsistencies in how safety systems treat equivalent content across scripts and languages. The preprint appears in both the AI and computational linguistics categories on arXiv.

safety/multilingual/hate-speech/evaluation
papersSEP 12 04:00 UTC

Study compares LLM adaptation methods for hate speech detection in Roman Urdu

A revised arXiv paper examines how large language models can be adapted to detect hate speech in Roman Urdu, a low-resource language written in Latin script. The authors compare several adaptation approaches, addressing challenges such as scarce annotated data, informal writing conventions, and the lack of standardized grammar. The work focuses on efficient methods suited to settings where labeled corpora are limited.

low-resource/hate-speech/fine-tuning/nlp