High-Value Training Data from Noisy, Diverse Sources

Hedi Driss, Evan Frick, Anastasios N. Angelopoulos +4

Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision. Data…