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Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
Abstract Predicting upcoming events is critical to our ability to effectively interact with our environment and conspecifics. In natural language processing, transformer models, which are trained on next-word prediction, appear to construct a general-purpose representation of language that can support diverse downstream tasks. However, we still lack an understanding of …