PaLM is a 540-billion parameter, densely activated Transformer language model trained on 780 billion tokens of high-quality text using the Pathways system, which enables efficient training across thousands of TPU v4 chips. PaLM achieves state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks, outperforming the finetuned state-of-the-art on multi-step reasoning tasks and average human performance on the BIG-bench benchmark. PaLM also excels in multilingual tasks and source code generation. The model demonstrates significant improvements in performance as scale increases, with some tasks showing discontinuous improvements. PaLM also shows strong capabilities in multilingual tasks and source code generation. The model was evaluated on a wide range of tasks, including English NLP tasks, BIG-bench, reasoning, code tasks, translation, multilingual natural language generation, and multilingual question answering. PaLM 540B outperforms prior state-of-the-art models on many tasks, including the SuperGLUE benchmark. The model also shows strong performance in multilingual tasks and source code generation. PaLM was trained on a diverse dataset including webpages, books, Wikipedia, news articles, source code, and social media conversations. The model was trained using a combination of model and data parallelism, and the training infrastructure was optimized for efficiency. PaLM 540B achieves high accelerator utilization due to its parallelism strategy and other factors. The model was evaluated on a wide range of tasks, including English NLP tasks, BIG-bench, reasoning, code tasks, translation, multilingual natural language generation, and multilingual question answering. PaLM 540B outperforms prior state-of-the-art models on many tasks, including the SuperGLUE benchmark. The model also shows strong performance in multilingual tasks and source code generation. PaLM was trained on a diverse dataset including webpages, books, Wikipedia, news articles, source code, and social media conversations. The model was trained using a combination of model and data parallelism, and the training infrastructure was optimized for efficiency. PaLM 540B achieves high accelerator utilization due to its parallelism strategy and other factors.PaLM is a 540-billion parameter, densely activated Transformer language model trained on 780 billion tokens of high-quality text using the Pathways system, which enables efficient training across thousands of TPU v4 chips. PaLM achieves state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks, outperforming the finetuned state-of-the-art on multi-step reasoning tasks and average human performance on the BIG-bench benchmark. PaLM also excels in multilingual tasks and source code generation. The model demonstrates significant improvements in performance as scale increases, with some tasks showing discontinuous improvements. PaLM also shows strong capabilities in multilingual tasks and source code generation. The model was evaluated on a wide range of tasks, including English NLP tasks, BIG-bench, reasoning, code tasks, translation, multilingual natural language generation, and multilingual question answering. PaLM 540B outperforms prior state-of-the-art models on many tasks, including the SuperGLUE benchmark. The model also shows strong performance in multilingual tasks and source code generation. PaLM was trained on a diverse dataset including webpages, books, Wikipedia, news articles, source code, and social media conversations. The model was trained using a combination of model and data parallelism, and the training infrastructure was optimized for efficiency. PaLM 540B achieves high accelerator utilization due to its parallelism strategy and other factors. The model was evaluated on a wide range of tasks, including English NLP tasks, BIG-bench, reasoning, code tasks, translation, multilingual natural language generation, and multilingual question answering. PaLM 540B outperforms prior state-of-the-art models on many tasks, including the SuperGLUE benchmark. The model also shows strong performance in multilingual tasks and source code generation. PaLM was trained on a diverse dataset including webpages, books, Wikipedia, news articles, source code, and social media conversations. The model was trained using a combination of model and data parallelism, and the training infrastructure was optimized for efficiency. PaLM 540B achieves high accelerator utilization due to its parallelism strategy and other factors.