3 papers
cs.LG2026
DatBench: Discriminative, Faithful, and Efficient VLM Evaluations
DatologyAI, :, Siddharth Joshi +30
Empirical evaluation serves as the primary compass guiding research progress in foundation models. Despite a large body of work focused on training frontier vision-language models…
cs.LG2025
BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining
DatologyAI, :, Pratyush Maini +28
Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, th…
cs.CL2025
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao +4
Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the…