1 citations · 2 across the 5 of their papers we have counts for
6 papers
Large Language Models Develop Belief State Geometry In-Context
Daniel Balcells, Andrew Jun Lee, Chirag Rastogi +3
Large language models (LLMs) trained on next-token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that support ICL remain poorly underst…
Transformers learn factored representations
Adam Shai, Loren Amdahl-Culleton, Casper L. Christensen +6
Transformers pretrained via next token prediction learn to factor their world into parts, representing these factors in orthogonal subspaces of the residual stream. We formalize tw…
Neural networks leverage nominally quantum and post-quantum representations
Paul M. Riechers, Thomas J. Elliott, Adam S. Shai
We show that deep neural networks, including transformers and RNNs, pretrained as usual on next-token prediction, intrinsically discover and represent beliefs over 'quantum' and 'p…
Next-token pretraining implies in-context learning
Paul M. Riechers, Henry R. Bigelow, Eric A. Alt +1
We argue that in-context learning (ICL) predictably arises from standard self-supervised next-token pretraining, rather than being an exotic emergent property. This work establishe…
Constrained belief updates explain geometric structures in transformer representations
Mateusz Piotrowski, Paul M. Riechers, Daniel Filan +1
What computational structures emerge in transformers trained on next-token prediction? In this work, we provide evidence that transformers implement constrained Bayesian belief upd…
Transformers represent belief state geometry in their residual stream
Adam S. Shai, Sarah E. Marzen, Lucas Teixeira +2
What computational structure are we building into large language models when we train them on next-token prediction? Here, we present evidence that this structure is given by the m…