Toxicity in language models is disproportionately encoded in early MLP layers and can be localized via activation differentials then suppressed at inference time without gradient descent.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
Opir introduces efficient multi-task encoder models trained on a 996-category safety taxonomy that match or exceed larger baselines on most safety benchmarks while using under 100M parameters for edge variants.
DExperts reaches 100% safety on explicit toxicity benchmarks but only 98.5% on implicit hate speech from ToxiGen while imposing a 10x latency increase on GPT-2.
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Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study
DExperts reaches 100% safety on explicit toxicity benchmarks but only 98.5% on implicit hate speech from ToxiGen while imposing a 10x latency increase on GPT-2.