3 papers
cs.LG2026
Fine-Tuning Dynamics of In-Context Factual Recall in Transformers
Ruomin Huang, Eshaan Nichani, Jason D. Lee +1
In-context learning \ -- performing tasks based on examples given in the prompt \ -- is an important capability that has emerged in large language models and has received significa…
cs.DS2026
Learning-Augmented Algorithms for -median via Online Learning
Anish Hebbar, Rong Ge, Amit Kumar +1
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introd…
cs.CL2025
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
Roy Xie, Junlin Wang, Ruomin Huang +5
The rapid scaling of large language models (LLMs) has raised concerns about the transparency and fair use of the data used in their pretraining. Detecting such content is challengi…