Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale on few-shot learning, we trained a 540-billion parameter,
densely activated, Transformer language model, which we call Pathways Language
Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML
system which enables highly efficient training across multiple TPU Pods. We
demonstrate continued benefits of scaling by achieving state-of-the-art
few-shot learning results on hundreds of language understanding and generation
benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough
performance, outperforming the finetuned state-of-the-art on a suite of
multi-step reasoning tasks, and outperforming average human performance on the
recently released BIG-bench benchmark. A significant number of BIG-bench tasks
showed discontinuous improvements from model scale, meaning that performance
steeply increased as we scaled to our largest model. PaLM also has strong
capabilities in multilingual tasks and source code generation, which we
demonstrate on a wide array of benchmarks. We additionally provide a
comprehensive analysis on bias and toxicity, and study the extent of training
data memorization with respect to model scale. Finally, we discuss the ethical
considerations related to large language models and discuss potential
mitigation strategies.

通过使用 Pathways ML 系统训练 PaLM，我们证明了模型规模对少样本学习的影响，PaLM 540B 在大量语言理解、生成基准测试中取得了突破性表现，并在多步推理任务中超越了微调表现，另外还有强大的多语种任务和源代码生成能力，以及对偏差和毒性的全面分析。