7 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.LG2026
From Simulation to Enaction: Post-trained language models recognize and react to their own generations
Asvin G., Jack Lindsey
Language models are pretrained as passive predictors with no incentive to model the consequences of their own outputs. Post-training changes this: a model producing its own respons…
cs.LG2024★ 7 cited
Task structure and nonlinearity jointly determine learned representational geometry
Matteo Alleman, Jack W Lindsey, Stefano Fusi
The utility of a learned neural representation depends on how well its geometry supports performance in downstream tasks. This geometry depends on the structure of the inputs, the…
cs.LG2023
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
Samuel Lippl, Jack W. Lindsey
Neural networks are often trained on multiple tasks, either simultaneously (multi-task learning, MTL) or sequentially (pretraining and subsequent finetuning, PT+FT). In particular,…