PaLM 2

PaLM (Pathways Language Model) is a large-scale language model, or LLM, analogous to theGPT family created by OpenAImaybeMeta's LLaMA family of models. 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 a variety of text generation and editing tasks. For example, you can train PaLM to be a conversational chatbot like ChatGPT, or you can use it for tasks like summarizing text or even writing code. (This is similar to the functionality Google also announced today for its Workspace applications like Google Docs and Gmail.)

At Google I/O 2023, Google CEO Pichai announced the launch of PaLM 2, a large model that benchmarks GPT-4, and the official release of a preview version with improved math, code, reasoning, multilingual translation, and natural language generation capabilities.

Google will offer four different sizes for PaLM 2, from smallest to largest: Gecko, Otter, Bison, and Unicorn.Gecko is lightweight enough to work on mobile devices and fast enough to run great interactive apps on devices even when offline. This versatility means that PaLM 2 can be fine-tuned to support the entire category in more ways, thus helping more people.

Characteristics of PaLM 2

PaLM 2 is Google's next-generation big language model with improved multilingualism, reasoning, and encoding capabilities.

• Multilingualism: PaLM 2 has received additional training in multilingual texts, covering more than 100 languages. This has significantly improved its ability to understand, generate and translate nuanced texts (including idioms, poems and riddles) in multiple languages, a difficult problem to solve.PaLM 2 has also passed an advanced language proficiency exam at the "Proficient" level.
• Reasoning: The extensive dataset of PaLM 2 includes scientific papers and web pages containing mathematical expressions. As such, it demonstrates improved capabilities in logic, common sense reasoning, and math.
• Programming: PaLM 2 is pre-trained on a large number of publicly available source code datasets. This means that it specializes in popular programming languages such as Python and JavaScript, but can also generate code specific to languages such as Prolog, Fortran, and Verilog.