“Connecting the Dots: LLMs can Infer & Verbalize Latent Structure from Training Data” by Johannes Treutlein, Owain_Evans
LessWrong (Curated & Popular)
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Exploration of LLMs' Inductive Out-of-Context Reasoning Abilities
The chapter delves into the inductive out-of-context reasoning abilities of large language models, showcasing their capacity to infer latent information and utilize it for downstream tasks without explicit reasoning. It presents experiments illustrating how these models can verbalize latent information and accomplish tasks like predicting city names and defining functions, even without in-context examples or structured thought processes.
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