Google’s CALM—The Solution to OpenAI?



Google’s new feature has the potential to revolutionize the field of large language models (LLMs). The breakthrough technology called CALM—Confident Adaptive Language Modeling—is designed to speed up LLMs like GPT-3 and LaMDA, without compromising performance levels.

What is CALM?

CALM is an advanced language model technology that Google has developed to improve its search engine’s ability to understand and interpret natural language queries. It stands for Continuous Adaptation for Language Model, which essentially means that the technology is continuously learning and adapting to improve its performance.

Google has been using language model technology for years, but CALM is a significant step forward as it is built on a neural network architecture that allows it to process natural language queries more efficiently. CALM uses a transformer-based model that can analyze and understand the context of a query, making it more capable of deciding which tasks need more effort. Like the human brain delegates energy so that we don’t put the same effort into pouring creamer in our coffees as we do writing a company-wide email, CALM, well, calms AI language models.

Generally speaking, LLMs are trained on massive amounts of text data in order to learn patterns and entity relationships in the language. For instance, the initial version of GPT was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totaling 3.3 billion words.

More recent LLMs, such as GPT-3, have been trained on even larger datasets. GPT-3 has more than 175 billion parameters and was trained on about 45 TB of text. The training data used for GPT-3 is not publicly disclosed, but it is believed to include a diverse range of sources, such as books, articles, and websites.

Now imagine all that data in a library. You sit alone in the library and suddenly, people start walking in the door with queries. “Tell me about the history of South America.” “What kind of dairy-free milk is best for me?” “How can my business benefit from using influencer marketing?” “Write me 10 options for social media copy” “Act as a journalist and write me copy about the impending recession.” You’d get a little overwhelmed, too, right? You have no idea how to prioritize these queries and you have to sift through millions of pieces of data to find just the right answer to present to the asker. 

This is what LLMs do every time we ask them to generate something—and why there may be points in the day when the platform asks you to return later due to high traffic. But if the LLMs had a way to sift through the data more efficiently—to know which parts of each query to prioritize, what needs “full effort” versus “partial effort”—they could be more effective.

The academic paper on CALM puts it this way: 

“Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks.

These gains come with a drastic increase in the models’ size, potentially leading to slow and costly use at inference time.

In practice, however, the series of generations made by LLMs is composed of varying levels of difficulty.

While certain predictions truly benefit from the models’ full capacity, other continuations are more trivial and can be solved with reduced compute.

…While large models do better in general, the same amount of computation may not be required for every input to achieve similar performance (e.g., depending on if the input is easy or hard).”

Red = Full Capacity/Green = Less Than Half Capacity

Google CALM

The above image shows this idea in action. Researchers wrote:

‘The colors represent the number of decoding layers used for each token—light green shades indicate less than half of the total layers. Only a few selected tokens use the full capacity of the model (colored in red), while for most tokens the model exits after one or few decoding layers (colored in green).”

The researchers also noted in their conclusion that implementing CALM in an LLM only requires minimal modification to help the language model increase speed. Essentially this allows LLMs to be trained faster and more efficiently, meaning they can process more information and produce more accurate results in less time. 

This has obvious implications for businesses in any industry, as it means they can gather insights and make decisions more quickly and with greater accuracy. But what does all this mean for B2B marketers? 

CALM’s Implications for B2B Marketers

Content Marketing

The CALM feature can have a significant impact on B2B content marketing strategies, as it can help marketers generate more accurate and relevant content based on real-time data and insights. With access to more and better data, LLMs could help marketers identify new trends and opportunities more quickly, allowing them to respond faster and stay ahead of the competition. This could be especially important in industries that are rapidly evolving or facing disruption.

Customer Engagement and Personalization

B2B marketers can improve customer engagement strategies by providing personalized content that resonates with their target audience.  LLMs can help identify patterns in customer behavior and preferences, allowing marketers to tailor their messaging and content more effectively. This can be especially important in industries with complex or technical products, where targeted messaging can make a big difference. Marketers can also leverage the technology to improve their customer service, by providing accurate and relevant responses to customer inquiries.


CALM technology can enhance the accuracy and effectiveness of automated translation tools, which can be invaluable for B2B companies that operate in global markets. By improving translation accuracy, CALM can enable B2B companies to communicate more effectively with their international customers and partners.

Of course, CALM is just one piece of the puzzle when it comes to B2B marketing. It’s important for marketers to keep up with all the latest developments in their field, from changing consumer behaviors to new marketing channels and marketing tactics. If you’d like some help mastering these latest AI developments in your marketing strategy, reach out


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