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LLMs implicitly encode uncertainty about numerical predictions in their embeddings, suggesting that expensive autoregressive sampling may be avoidable.
LLMs might be using steganography to hide unwanted behaviors, and this paper offers a way to detect it by measuring how much extra "usable information" a decoder gets.
