Summary
This discussion explores whether language modeling is fundamentally token-level or sequence-level, noting that pretraining and sampling suggest a token-level view while alignment points to a sequence-level understanding. Despite textbook definitions favoring sequence-level, practical cross-entropy loss implementations operate at the token level. The author seeks to unify these perspectives and determine the more principled framing for the field.
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