Transformers' In-Context Learning Scaling for Gaussian-Mixture Tasks
An arXiv preprint (2604.25858) has been released, detailing a thorough empirical investigation into in-context learning (ICL) within transformers, specifically for Gaussian-mixture binary classification. The authors build upon the theoretical foundation laid by Frei and Vardi (2024) to explore how factors like input dimension, the quantity of in-context examples, and the number of pre-training tasks influence test accuracy. Through a controlled synthetic environment and a linear classifier approach, they identify the geometric conditions necessary for effective inference. This research fills a gap in the comprehension of ICL's empirical scaling behavior, which previous theories had addressed for linear classification but had not completely defined for more intricate tasks.
Key facts
- arXiv preprint 2604.25858 investigates in-context learning scaling
- Study focuses on Gaussian-mixture binary classification tasks
- Builds on theoretical framework by Frei and Vardi (2024)
- Analyzes dependence on input dimension, in-context examples, and pre-training tasks
- Uses controlled synthetic setup and linear classifier formulation
- Isolates geometric conditions for successful inference
- Addresses gap in empirical scaling behavior of ICL
- Prior theory established conditions for linear classification ICL
Entities
Institutions
- arXiv