In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model
Sequence models can generalise in fundamentally distinct ways: extrapolating structures seen in training, or using their prompt to adapt to novel tasks. The prevailing strategy determines how the model behaves beyond its training distribution, controlling potential capabilities and risks. Recent work has characterised many different strategies theoretically, and empirically described the transition between them as the quality and quantity of pretraining data vary. However, there is no learning theory for this competition, hence no fundamental understanding of \textit{why} one solution might be favoured over another. Here we provide such a theory in a solvable topic model---documents drawn...
| Authors | Francesco Cagnetta, Casper L. Christensen |
|---|---|
| Primary category | cs.LG |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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