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PaLM 2 Training Data Parameters – Is it 540 billion?



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Google has added a new product to the list of AI inventions. The company launched its latest large language model (LLM), PaLM 2, at the Google I/O conference 2023. The news was announced by Google’s CEO Sundar Pichai who also mentioned that the company has been testing the model for the past few months.

Google has also released a 92-page long technical report specifying the technical details and performance of PaLM 2. The downside of this report is that it lacks transparency regarding the PaLM 2 training parameters. However, the company says PaLM 2 is much smaller and more efficient than its predecessor PaLM. It is also trained on a massive dataset to provide accurate and faster responses.

This post will give an overview of the PaLM 2 parameters and how the LLM compares with Open AI’s LLMs.

PaLM 2 Training Data Parameters - Is it 540 billion?

How much data is PaLM 2 trained on?

Google’s technical report doesn’t mention many details regarding the PaLM 2 training dataset. However, PaLM, the previous version of PaLM 2, was trained with 540 billion parameters. According to Google, the current version is trained using fewer parameters but with a much more efficient method.

The PaLM 2 training dataset comprises information from various sources, including web pages, research papers, books, code, and mathematical and conversational data. The information is fed into the LLM in English and non-English languages to make it multilingual. The training dataset allows the model to respond better to coding and reasoning questions.

How much deeper understanding does PaLM 2 have of mathematics, logic, and science?

PaLM 2 developers are focusing on making the language model more powerful. They have trained it with a massive dataset to deeply understand mathematics, logic, and science questions.

Google has compared the performances of PaLM 2, PaLM, and GPT-4 based on their reasoning abilities. Google PaLM 2 clearly outperforms the rest two models across various parameters. It gives exceptional results for under-represented languages.

The model also gives excellent results when used for mathematical reasoning. It outperforms PaLM when tested across various datasets, including boolean expressions, casual judgment, geometrical shapes, word sorting, etc. It also beats Minerva, GPT-4, and SOTA.

The model and its capabilities have been tested using knowledge from various books and research papers. It was trained using different methods to give accurate responses. The model also responds to longer context and dialog for any subject.

Palm 2 vs GPT 4 parameters

GPT-4 is the latest language model developed by Open AI, and Google developed PaLM 2. Both language models are trained with millions of parameters to respond to human queries and build generative AI apps. They possess similar functionalities, but their performances vary across several parameters. 

Initially, GPT-4 was anticipated to have been trained with 1 trillion parameters. However, Open AI has announced nothing regarding the training dataset size. The company has maintained silence regarding the dataset parameters and the model size.

Likewise, Google hasn’t revealed the size of PaLM 2 and its training dataset. However, PaLM 2 is much smaller and more efficient than GPT-4. Google has also confirmed the same within its technical report.

The report provides a detailed comparison between GPT-4 and PaLM 2 across various parameters. PaLM 2 outperforms GPT-4 in reasoning, mathematics, multilingualism, and translation. GPT-4 gives great results when tested for image-based inputs, while PaLM 2 lacks behind.

Further, PaLM 2 has a smaller, faster, and more efficient sub-version, Gecko, which makes the model ideal for mobile devices. It can also work offline. Contrarily, GPT-4 requires a working internet connection and doesn’t support mobile device usage.

How parameters affect the performance of PaLM 2

PaLM 2 is trained with a smaller dataset and fewer parameters but with a more efficient approach. This gives the model the flexibility to generate faster responses. It also makes it lightweight and ideal for mobile devices.

Consumers, developers, and enterprises of all sizes worldwide can access the model. It can also be integrated into various AI applications to give users a better experience.

How PaLM 2 competes directly with OpenAI’s LLMs

PaLM 2 is Google’s latest LLM and a close competitor of Open AI’s recent LLM, GPT-4. PaLM 2 is trained using a similar dataset comprising various sources, including web pages, books, research papers, etc. Google has also integrated PaLM 2 to Bard, like Open AI has integrated GPT-4 to ChatGPT-4.

Both PaLM 2 and GPT-4 are accessible by a limited group of people. Google plans to integrate PaLM 2 into other Google products and services in the future to make it easily accessible. PaLM 2 is also available in four different versions, whereas Gecko, the smallest one, can be used for mobile devices.

How many languages is PALM 2 trained in?

PaLM 2 is a multilingual language model that understands over 100 languages. It understands Arabic, French, German, English, Turkish, Russian, Portuguese, and Korean. The model understands under-represented languages like Swahili and Haitian.

It also understands the toxic words used in various languages and avoids them. Google has also tested its capabilities by measuring its performance through language proficiency tests.

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