PhD student, CMU, Carnegie Mellon University
3 papers at NeurIPS 2025
We propose a new framework and set of evaluation criteria to assess the utility of text embeddings used in data selection for pretraining langauge models.
We present a data-centric pretraining framework that builds safety into the model from the start
We reliably predict the behavior of black-box language models by training predcitors on their responses to follow-up questions.
