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It is suggested that LLMs can serve as auxiliary annotators for sensitive-language detection in historical materials, provided that human oversight and contextual interpretation remain central to annotation workflows.
The study traces this dialect tax across the natural language processing pipeline, suggesting that the dialect tax is encoded and accumulated not by any one step in isolation, but at every step of the language modeling process.
This work introduces Code Monitor Red Teaming, a monitor-red-teaming protocol that fixes a public-check information boundary while varying generator pressure, verifier scaffolding, and weak-to-strong capability.
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