PaLM 2
PaLM (Pathways Language Model) is a large-scale language model, also known as LLM, similar to the GPT series or Meta's LLaMA series models created by OpenAI.
Tags:AI Training ModelAI Model modelPaLM (Pathways Language Model) is a large-scale language model, also known as LLM, similar to the GPT series or Meta’s LLaMA series models created by OpenAI. Google first announced the launch of PaLM in April 2022, with over 540 billion training parameters. Like other LLMs, PaLM is a flexible system that can perform various text generation and editing tasks. For example, you can train PaLM into a conversational chatbot like ChatGPT, or you can use it for tasks such as summarizing text or even writing code. (This is similar to the features announced by Google today for its workspace applications such as Google Docs and Gmail.)
At the 2023 Google I/O Conference, Google CEO Pichai announced the launch of the PaLM 2, a large model that benchmarks against GPT-4, and officially released a preview version, improving mathematical, coding, reasoning, multilingual translation, and natural language generation capabilities.
Google will offer PaLM 2 in four different sizes, from smallest to largest: Gecko, Otter, Bison, and Unicorn. Gecko is very lightweight, capable of working on mobile devices, and fast enough to run excellent interactive applications on the device even when offline. This versatility means that PaLM 2 can be fine tuned to support the entire category of products in more ways, thereby helping more people.
Characteristics of PaLM 2
PaLM 2 is Google’s next-generation big language model with improved multilingual, inference, and encoding capabilities.
Multilingualism: PaLM 2 has received more training in multilingual texts, covering over 100 languages. This significantly improves its ability to understand, generate, and translate nuanced texts (including idioms, poetry, and riddles) in multiple languages, which is a difficult problem to solve. PaLM 2 also passed the advanced language proficiency test at the proficiency level.
Inference: The extensive dataset of PaLM 2 includes scientific papers and web pages containing mathematical expressions. Therefore, it demonstrates improved abilities in logic, common sense reasoning, and mathematics.
Programming: PaLM 2 was pre trained on a large number of publicly available source code datasets. This means it excels in popular programming languages such as Python and JavaScript, but can also generate specialized code for languages such as Prolog, Fortran, and Verilog.
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