9 citations · 16 across the 5 of their papers we have counts for
6 papers · 1 filter
In-context classification: Calibration and noise in linear attention with softmax readout
Arman Rysmakhanov, Mary Letey, Yue M. Lu +2
We study in-context binary classification in a simple attention model trained to minimise a logistic loss. Each prompt has a latent logistic teacher, and the learner must infer its…
What can linear attention learn from nonlinear teachers in-context?
Mary Letey, Arman Rysmakhanov, Yue M. Lu +2
Linear attention is a tractable model for understanding the mechanisms governing in-context learning in transformers. For linear regression tasks, recent asymptotic analyses have c…
Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch
Mary Letey, Yue M. Lu, Cengiz Pehlevan +1
Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability…
Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time
Blake Bordelon, Mary I. Letey, Cengiz Pehlevan
We study in-context learning (ICL) of linear regression in a deep linear self-attention model, characterizing how performance depends on various computational and statistical resou…
Pretrain-Test Task Alignment Governs Generalization in In-Context Learning
Mary I. Letey, Jacob A. Zavatone-Veth, Yue M. Lu +1
In-context learning (ICL) is a central capability of Transformer models, but the structures in data that enable its emergence and govern its robustness remain poorly understood. In…
Asymptotic theory of in-context learning by linear attention
Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth +2
Transformers have a remarkable ability to learn and execute tasks based on examples provided within the input itself, without explicit prior training. It has been argued that this…