# PaLM: Scaling Language Modeling with Pathways

**Authors:** Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif  
**arXiv:** [2204.02311](https://arxiv.org/abs/2204.02311)  
**Date:** 2022-04-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2204.02311v1/

## Key points

- Researchers trained a 540-billion parameter language model called Pathways Language Model (PaLM) using few-shot learning.
- PaLM was efficiently trained on 6144 TPU v4 chips using the Pathways ML system.
- Scaling up the model size resulted in breakthrough performance in language understanding and generation tasks.
- PaLM 540B outperformed state-of-the-art models on multi-step reasoning tasks and surpassed average human performance on the BIG-bench benchmark.
- The model showed strong capabilities in multilingual tasks and source code generation.
- Comprehensive analyses were conducted on bias, toxicity, and training data memorization with respect to model scale.
- Ethical considerations related to large language models were discussed.
- Potential mitigation strategies for ethical concerns were proposed.

## Summary

In this study, the researchers trained a 540-billion parameter language model called Pathways Language Model (PaLM) using few-shot learning. They used the Pathways ML system to efficiently train PaLM on 6144 TPU v4 chips. The results showed that scaling up the model size led to breakthrough performance in various language understanding and generation tasks. PaLM 540B outperformed the finetuned state-of-the-art models on multi-step reasoning tasks and even surpassed average human performance on the BIG-bench benchmark. The model also demonstrated strong capabilities in multilingual tasks and source code generation. Additionally, the researchers conducted comprehensive analyses on bias, toxicity, and training data memorization with respect to model scale. They also discussed ethical considerations related to large language models and proposed potential mitigation strategies.