How Google’s PaLM redefined scale, effectivity, and emergent reasoning in massive language fashions with a primary massive scaled use case of Google’s Pathway system.
📄 Chowdhery et al., “PaLM: Scaling Language Modeling with Pathways” (2022)
tl;dr: The paper that showcases PaLM to the world as a next-generation massive language mannequin that achieves state-of-the-art outcomes by combining huge scale with the Pathways system for environment friendly and versatile coaching.
📄 “GShard: Scaling Large Fashions with Conditional Computation” (2020) — precursor to Pathways.
tl;dr: Launched mixture-of-experts routing, which impressed Pathways and sparse coaching in PaLM.
📄 “Swap Transformers” (2021) — sparse professional fashions idea.
tl;dr: PaLM was constructed on TPU v4 Pods utilizing Pathways, with a concentrate on sparse coaching and optimized scaling. Improvements in immediate engineering and infrastructure made its efficiency doable.
📄 “Consideration Is All You Want” (Vaswani et al., 2017)
tl;dr: Launched the Transformer structure that underpins PaLM and most fashionable LLMs.
