Apple Workshop on Natural Language and Interactive Systems 2025: Opening Up the Full Language Model Pipeline: OLMo and Tülu
AuthorsHanna Hajishirzi (University of Washington)
Apple Workshop on Natural Language and Interactive Systems 2025: Opening Up the Full Language Model Pipeline: OLMo and Tülu
AuthorsHanna Hajishirzi (University of Washington)
When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs
August 13, 2026research area Data Science and Annotation, research area Privacy
As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking whether points that have a negligible impact on the model’s learning need to be removed. Through a…
Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, several works have demonstrated the possibility of generating discrete data continuously by a simple flow matching process between a Gaussian and the one-hot encoded data…