Evaluating Large-Scale Learning Systems

Explore challenges and solutions in evaluating large-scale machine learning systems through this insightful conference talk by Virginia Smith. Delve into the complexities of assessing models trained in federated networks of devices, addressing issues such as device subsampling, heterogeneity, and privacy that can impact evaluation reliability. Discover ReLM, a system designed for validating and querying large language models (LLMs), which utilizes regular expressions to enable faster and more effective LLM evaluation. Learn about the importance of faithful evaluations in deploying machine learning models and gain insights into addressing concerns such as data memorization, bias, and inappropriate language in LLMs. Recorded at SPCL_Bcast #41 on October 13, 2023, this 59-minute talk provides valuable knowledge for researchers and practitioners working with large-scale learning systems.

[SPCL_Bcast] Evaluating Large-Scale Learning Systems