3 papers
cs.LG2026
Learning from Synthetic Data: Limitations of ERM
Kareem Amin, Alex Bie, Weiwei Kong +2
The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, "natural" content has been contaminated by data points that appea…
cs.LG2025
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
Kareem Amin, Sara Babakniya, Alex Bie +3
Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown…
cs.LG2025
Clustering and Median Aggregation Improve Differentially Private Inference
Kareem Amin, Salman Avestimehr, Sara Babakniya +4
Differentially private (DP) language model inference is an approach for generating private synthetic text. A sensitive input example is used to prompt an off-the-shelf large langua…