3 papers
stat.ML2025
Analyzing limits for in-context learning
Omar Naim, Jerome Bolte, Nicholas Asher
Our paper challenges claims from prior research that transformer-based models, when learning in context, implicitly implement standard learning algorithms. We present empirical evi…
cs.LG2024
Re-examining learning linear functions in context
Omar Naim, Guilhem Fouilhé, Nicholas Asher
In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. However, our understanding of how ICL works remains…
cs.CL2024
On Explaining with Attention Matrices
Omar Naim, Nicholas Asher
This paper explores the much discussed, possible explanatory link between attention weights (AW) in transformer models and predicted output. Contrary to intuition and early researc…