This article argues against viewing Large Language Models (LLMs) solely as next-token predictors. It suggests that this limited perspective fails to capture the full complexity and emergent capabilities of these models. Instead, the author proposes considering LLMs as systems that develop internal representations and understanding, enabling them to perform tasks beyond simple sequential prediction. AI
IMPACT Challenges the fundamental understanding of LLM mechanics, potentially influencing future research directions and applications.
RANK_REASON Article presents an opinion/analysis on the nature of LLMs.
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