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Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The next-token prediction objective naturally identifies a token's contribution with its own loss. However, each token is not only a prediction target but also context for...
It is shown that preference alignment preserves the human response distribution only under a restrictive condition, and no consistent evidence that real human preferences satisfy it, and human-likeness is established as an explicit dimension of alignment rather than something assumed to follow from preference alignment...
Label Wave is proposed, which does not require validation data for selecting the desired model across various weakly supervised learning paradigms, including learning with noisy labels (LNL), positive-unlabeled learning, and unlabeled-unlabeled learning.
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