Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models
The next-token prediction (NTP) objective trains language models to predict a single token at each step, even though many continuations can express the same meaning. For example, in the sentence ``this sticker can be placed here'', positioned, attached, or put are all plausible alternatives. While standard NTP training treats these alternatives as mutually exclusive targets, we explore a self-supervised framework that encourages models to predict concepts, approximated as sets of semantically equivalent tokens. Models trained with this concept supervision align better with human similarity judgments, improve classification, clustering, and reranking performance, and achieve comparable or ...
| Authors | Christine Zhang, Dan Jurafsky, Chen Shani |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 30 August 2026 |
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