PaLM 2 It is the next generation of large-scale language models launched by Google.Expertise in advanced reasoning tasks, including code and mathematics, classification and question answering, translation and multilingualism, and natural language generation.

Google claims that PaLM 2 is a state-of-the-art language model, outperforming all previous LLMs, including PaLM. Currently, PaLM 2 is used in Med-PaLM 2 and Sec-PaLM, and powers Google’s generative AI functions and tools such as Bard and the PaLM API.

reasoning:PaLM 2 can decompose complex tasks into simpler subtasks and is better at understanding the nuances of human language than previous LLMs such as PaLM. For example, PaLM 2 is good at understanding riddles and idioms, which requires understanding the ambiguity and figurative meaning of words rather than their literal meaning.

Multilingual translation:PaLM 2 was trained on a corpus of more than 100 languages, making PaLM 2 good at multilingual tasks, including finer phrasing than previous models.

coding:PaLM 2 can also understand, generate and debug code and has been pre-trained in more than 20 programming languages. This means it excels at popular programming languages ​​like Python and JavaScript, but is also capable of generating specialized code in languages ​​like Prolog, Fortran, and Verilog. Combining this with its language capabilities can help teams collaborate across languages.

PaLM 2 is good at tasks like high-level reasoning, translation, and code generation because of the way it is built. It improves upon its predecessor, PaLM, by unifying three distinct research advances in large language models:

  • Using Computational Optimal Scaling: The basic idea of ​​Computational Optimal Scaling is to scale model size and training dataset size proportionally. This new technology makes PaLM 2 smaller than PaLM, but more efficient, with better overall performance, including faster inference, fewer parameters to serve, and lower cost to serve.
  • Improved dataset mixing: Previous LLMs, such as PaLM, used pre-training datasets that were mainly English text. PaLM 2 improves its corpus with more languages ​​and a diverse pre-training mix that includes hundreds of human and programming languages, mathematical equations, scientific papers, and web pages.
  • Updated model architecture and objectives: PaLM 2 has an improved architecture and is trained on a variety of different tasks, all of which help PaLM 2 learn different aspects of language.

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