Language models are advancing faster than ever, but what’s driving this rapid improvement? This episode dives into the findings of a recent study that quantifies the impact of algorithmic advancements on language models from 2012 to 2023. We’ll explore how innovations in pre-training algorithms are helping models achieve the same performance with drastically less compute, doubling efficiency every 8 months on average. Join us as we uncover the balance between scaling up compute resources and refining algorithms and what this means for the future of AI in areas like NLP, machine translation, and beyond.
Download Link:https://arxiv.org/pdf/2403.05812v1.pdf)
