The concept of Large Language Models (LLMs) being solely "next token predictors" is misleading, according to a LessWrong post. While pre-training involves predicting the subsequent token in a sequence, this process forces models to learn complex language structures, grammar, and even factual information to make accurate predictions. The author argues that this training regime, when applied extensively, pushes LLMs beyond simple guessing and towards a more sophisticated understanding. AI
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IMPACT Challenges a common framing of LLM capabilities, potentially influencing how their limitations are discussed.
RANK_REASON Opinion piece arguing against a common framing of LLMs.