PaLM 2: Google’s Advanced Language Model for Multilingual Capabilities

Google I/O 2023 was an eagerly awaited event for tech enthusiasts and developers worldwide. Among the exciting announcements made during the conference, one stood out: the unveiling of PaLM 2, Google’s newest large language model (LLM).

PaLM 2 serves as the bedrock for various cutting-edge Google products, including Google Generative AI Search, Duet AI in Google Docs and Gmail, and Google Bard, to name a few.

In this article, we’ll delve into the details of the PaLM 2 AI model, comparing it to its predecessor and addressing common questions surrounding its capabilities.

What is Google’s PaLM 2 AI Model?

PaLM 2 represents Google’s latest advancement in large language models. It excels in advanced reasoning, coding, and mathematics while boasting multilingual support for over 100 languages. PaLM 2 is the successor to the original Pathways Language Model (PaLM) introduced in 2022.

While the initial PaLM model was trained on a staggering 540 billion parameters, Google’s PaLM 2 takes a different approach, focusing on smaller size, enhanced speed, and improved efficiency.

What are the Parameters and Size of Google’s PaLM 2

Google has not officially disclosed the parameter size of PaLM 2 in its technical report. However, according to a TechCrunch report, one variant of the PaLM 2 model has been trained on 14.7 billion parameters, significantly smaller than PaLM 1 and many other competing models.

There is speculation among researchers on Twitter that the largest PaLM 2 model may have been trained on around 100 billion parameters, still considerably fewer than the parameter count of OpenAI’s GPT-4, which is rumored to be trained on a mind-boggling 1 trillion parameters.

Google emphasizes that size doesn’t always equate to better performance. The company believes that research creativity plays a pivotal role in building exceptional models. While the specific research creativity techniques employed in PaLM 2 are not disclosed, it is likely that Google leverages methods such as Reinforcement Learning from Human Feedback (RLHF), compute-optimal scaling, and other novel approaches.

By utilizing these techniques, Google aims to achieve impressive results with a relatively smaller model.

What are the Features of PaLM 2?

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PaLM 2 brings forth a host of remarkable features that set it apart. Firstly, it excels in common sense reasoning, with Google stating that PaLM 2’s reasoning capabilities are competitive with those of GPT-4. In evaluation tests like WinoGrande commonsense and ARC-C, PaLM 2 outperforms GPT-4 in terms of reasoning abilities. It also performs well in other reasoning tests such as DROP, StrategyQA, and CSQA.

Additionally, PaLM 2’s multilingual capabilities are noteworthy. It can understand idioms, poems, nuanced texts, and even riddles in various languages. PaLM 2 surpasses the literal meaning of words and comprehends the ambiguous and figurative aspects of language.

This proficiency is due to its pre-training on parallel multilingual texts and the utilization of a vast corpus of high-quality multilingual data. As a result, translation and other language-related applications demonstrate superior performance when using PaLM 2.

Moreover, PaLM 2 boasts impressive coding capabilities. It has been trained on extensive source code datasets available in the public domain, enabling it to support over 20 programming languages.

From popular languages like Python and JavaScript to older ones like Prolog, Fortran, and Verilog, PaLM 2 covers a wide range of coding languages. It can generate code, provide context-aware suggestions, facilitate code translation, and even add functions based on comments.

Different PaLM 2 Models

To cater to diverse use cases, Google has developed four distinct PaLM 2 models: Gecko, Otter, Bison, and Unicorn. Gecko, the smallest model, is lightweight and can run efficiently even on smartphones, without relying on an active internet connection.

It can process 20 tokens per second on a flagship phone, equivalent to approximately 16 words per second. This opens up exciting possibilities for on-device AI-powered applications that don’t require extensive resources.

In addition to Gecko, Google has introduced Med-PaLM 2, a medical-specific LLM fine-tuned on PaLM 2. Med-PaLM 2 has achieved an “Expert” level competency on the U.S. Medical Licensing Exam-style questions, surpassing GPT-4’s accuracy.

It demonstrates immense potential in the medical field, especially when coupled with its multimodal capabilities. Med-PaLM 2 can analyze medical images like X-rays and mammograms, providing expert-level conclusions akin to skilled clinicians. This breakthrough can bring invaluable medical access to remote areas.

Furthermore, Google has developed Sec-PaLM, a specialized version of PaLM 2 tailored for cybersecurity analysis. With its exceptional capabilities, Sec-PaLM can swiftly detect and analyze malicious threats, bolstering security measures in various domains.

PaLM 2 in Google Products

Google has integrated PaLM 2 into several of its popular products. One such example is Google Bard, an interactive AI chatbot. Recently, Google migrated Bard to PaLM 2 and expanded its accessibility to over 180 countries. Users can experience the power of PaLM 2 by interacting with Google Bard.

Moreover, PaLM 2 plays a crucial role in enhancing the AI capabilities of Gmail and Google Workspace (including Google Docs and Google Sheets) through a feature called Duet AI. However, access to these AI-powered features in Gmail, Google Docs, and Google Sheets is currently available through a waitlist.

For developers, Google has also released the PaLM API, which is built on the PaLM 2 model. This API allows developers to integrate the power of PaLM 2 into their own applications and products. With the ability to generate more than 75 tokens per second and a context window of 8,000 tokens, the PaLM API provides developers with a versatile and efficient tool.


Google’s PaLM 2 AI model is an impressive advancement in the field of large language models. It showcases the power of research creativity in building efficient and capable models. Despite its relatively smaller size compared to other models like GPT-4, PaLM 2 brings forth a wide range of features, including common sense reasoning, multilingual capabilities, and advanced coding proficiency.

With different PaLM 2 models tailored for specific use cases, such as medical knowledge and cybersecurity analysis, Google has successfully expanded the application of PaLM 2 across various domains. Integration into popular Google products like Google Bard, Gmail, and Google Workspace further demonstrates the versatility and impact of PaLM 2.

As the field of AI continues to evolve, models like PaLM 2 pave the way for more accessible and efficient AI-powered applications and services. With its impressive capabilities and ongoing advancements, PaLM 2 is set to play a significant role in shaping the future of language understanding and AI integration.

Faqs About Google PaLM 2

While both PaLM 2 and GPT-4 have their strengths, PaLM 2 showcases exceptional common sense reasoning capabilities. However, GPT-4’s larger parameter size gives it an edge in certain areas. It ultimately depends on the specific use case and requirements.

The details regarding plugin support in PaLM 2 are not explicitly mentioned. However, PaLM 2’s focus on efficiency and adaptability makes it likely that plugin support could be explored or implemented in the future.

Yes, PaLM 2 can be fine-tuned for domain-specific applications. Google has already demonstrated this with Med-PaLM 2, a medical-specific LLM fine-tuned on PaLM 2 that achieved impressive results in medical knowledge evaluations.

PaLM 2 supports over 20 programming languages, including popular languages like Python and JavaScript, as well as older languages like Prolog, Fortran, and Verilog. Its wide language support makes it a versatile tool for developers working with different programming languages.

Google has introduced four different PaLM 2 models: Gecko, Otter, Bison, and Unicorn. These models vary in size and capabilities, with Gecko being the smallest and Unicorn being the largest. Each model caters to specific needs and use cases.

While PaLM 2 primarily focuses on language understanding and reasoning, Google has developed specialized versions like Med-PaLM 2, which can analyze medical images such as X-rays and mammograms. This demonstrates the potential for PaLM 2 to expand into multimodal tasks beyond text.

Developers can utilize the power of PaLM 2 through the PaLM API, which provides an interface to integrate PaLM 2 into their applications and products. By signing up for the PaLM API, developers can leverage PaLM 2’s capabilities in their own projects.

Yes, individual consumers can experience the benefits of PaLM 2 through Google Bard, an interactive AI chatbot powered by PaLM 2. Additionally, AI-powered features in Gmail, Google Docs, and Google Sheets (known as Duet AI for Google Workspace) are accessible through a waitlist.

PaLM 2’s smaller size allows for faster response times and more efficient computation. It offers the convenience of processing queries and providing multiple drafts promptly. The smaller size also contributes to lower serving costs, making PaLM 2 a cost-effective solution.

To get started with PaLM 2, you can access Google Bard and follow the instructions provided to interact with the AI chatbot. For AI-powered features in Gmail, Google Docs, and Google Sheets, you can join the waitlist and be notified when access becomes available. Developers can sign up for the PaLM API to integrate PaLM 2 into their applications and explore its potential.

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