PaLM: Scaling Language Modeling with Pathways

Overview

PaLM is a 540-billion parameter large language model developed by Google, trained using the Pathways system to efficiently scale across multiple TPU pods. It achieves strong performance on reasoning, code generation, and translation tasks through a combination of dense and sparse attention mechanisms.

Best for

Best for
Researchers studying large-scale language model scaling and few-shot reasoning

Use cases

• Few-shot reasoning and chain-of-thought prompting for complex tasks
• Code generation and understanding across multiple programming languages
• Multilingual translation and natural language understanding benchmarks

Notes

PaLM is a 540-billion parameter large language model developed by Google, trained using the Pathways system to efficiently scale across multiple TPU pods. It achieves strong performance on reasoning, code generation, and translation tasks through a combination of dense and sparse attention mechanisms.

Use cases

• Few-shot reasoning and chain-of-thought prompting for complex tasks
• Code generation and understanding across multiple programming languages
• Multilingual translation and natural language understanding benchmarks

Pros

• State-of-the-art results on many NLP benchmarks at time of release
• Efficient training via Pathways enables scaling to 540B parameters
• Strong performance on reasoning tasks with chain-of-thought prompting

Cons

• Not publicly available as a standalone model or API
• Requires massive computational resources to run inference
• Limited to research community access through Google’s infrastructure

Indexed from awesome-llm and enriched against its public facts.