PaLM, or Pathways Language Model, is a large language model (LLM) developed by Google AI and is one of the top NLP language models transforming AI in 2025.
It was announced in April 2022 and is currently the largest LLM in the world, with 540 billion parameters. PaLM is trained on a massive dataset of text and lines of code.
This AI model, similar to GPT-3 and GPT-4 models, is designed to perform well at a wide variety of tasks, including natural language understanding and generation, multi-step reasoning tasks, and code generation.
Today, you can use PaLM through applications such as Google Bard AI to:
- Generate completely unique text content
- Translate content into other languages with high accuracy
- Create different kinds of creative content, such as poems, code, scripts, musical pieces, emails, letters, etc.
- Answer your questions in an informative way, even if they are open-ended, challenging, or just strange.
- Generate code, similar to OpenAI’s Codex model that powers code generation in chatbots like ChatGPT
PaLM is still under development, but it has the potential to revolutionize the way we interact with computers. It could be used to create new forms of communication and collaboration and to automate tasks that are currently done by humans.
About PaLM training
PaLM was trained on a massive dataset of text and code, including books, articles, web documents, lines of code of repositories like GitHub, conversations, etc.
The training was scaled using data parallelism at the Pod level across two Cloud TPU v4 Pods while using standard data and model parallelism within each Pod.
PaLM was developed on a system called Pathways. Pathways is a system for training large language models that is designed to be efficient and scalable. Pathways uses a technique called distributed training, which allows the model to be trained on multiple GPUs at the same time.
How PaLM is better than other language models
Here are some of the differences between PaLM and other large language models:
- Size: PaLM is the largest language model in the world, with 540 billion parameters. This is significantly larger than other large language models, such as GPT-3, which has 175 billion parameters.
- Training method: PaLM was trained using a technique called masked language modeling. This is a more effective training method than the technique used to train other large language models.
- Performance: PaLM has been shown to outperform other large language models on a variety of tasks, including text generation, translation, and question answering.
Through the fusion of model scale and chain-of-thought prompting, PaLM demonstrates remarkable advancements in tackling reasoning tasks that involve multi-step arithmetic or common-sense reasoning. Unlike previous language models such as Gopher, which showed limited improvement in performance with increased model scale, PaLM leverages its enhanced size to great effect.
Read the official research release
