Why Google Struggles to Catch up with PaLM 2 vs GPT-4?” – A Comparative Review.

Artificial intelligence has revolutionized the way we interact with technology and has become an essential tool in various fields. PaLM 2 and GPT-4 are two of the most advanced language models in the market, and Google has been striving to catch up. In this comparative review, we’ll explore why Google is struggling to keep up with PaLM 2 and GPT-4, delving into the intricacies of their architecture and training models. Join us as we unravel the secrets behind these powerful language models and understand the impact they’re having on the field of artificial intelligence.

Why Google Struggles to Catch up with PaLM 2 vs GPT-4?” – A Comparative Review

It’s common knowledge that Google is one of the most innovative and forward-thinking tech companies in the world. Yet, despite Google’s resources and expertise, it seems to be struggling to catch up with PaLM 2, especially when compared to GPT-4. In this article, we will explore why Google is lagging behind and the impact of artificial intelligence in bringing improvements.


Google, in its I/O stream, referenced artificial intelligence (AI) multiple times. AI has become a vital tool in various industries, including education, healthcare, and finance. It helps automate tasks, improve efficiency, and provide accurate predictions. It’s no surprise that Google, among other tech giants, invests heavily in AI research to maintain its superior position.

PaLM 2 vs GPT-4

PaLM 2 is Google’s newest AI research paper, and it has been confirmed that it’s incredible. It’s at least as good as GPT-4 in many tests. The ability of large models to reason is critical, and Google has been exploring this area to improve its performance in language modeling tasks.

Coding tests are used to evaluate language models, and before PaLM 2, the existing scores on human evaluations were higher for GPT-4. However, despite Google’s resources, PaLM 2 has managed to catch up with GPT-4 and is even surpassing it in some areas.

Google decided to go with 100 tries in its model, which is unusual compared to GPT-4’s approach. However, with various coding tools, PaLM 2 has been able to use its advantage to provide a better language model.

Despite all efforts made by Google to create something like GPT-4, it seems to be falling short. PaLM 2 is beating GPT-4 on several metrics, but with some tricks. Instruction tune, variant chain of thought, and self-consistency are some of the techniques used to achieve better results in PaLM 2.

The Impact of AI in Bringing Improvements

AI has been critical in bringing about improvements in various fields. In the language modelling field, it has enabled companies like Google to create better models that can reason and provide more accurate predictions. Large models can now reason in ways that were previously impossible, enabling them to bridge the gap between human reasoning and machine reasoning.

AI algorithms have also enabled accurate predictions in areas such as healthcare, finance, and weather forecasting, among others. It has been used to identify trends and patterns, providing insights that were previously not apparent. AI-powered machines now augment human intelligence, enabling us to make faster and more accurate decisions.


In conclusion, it’s evident that AI has become a vital tool in bringing about improvements in various fields. Google’s recent research paper, PaLM 2, has given it an edge in language modelling, enabling it to catch up with and even outperform GPT-4 in some areas. While PaLM 2 uses different techniques to achieve better results, it’s clear that the ability of large models to reason is essential.

As AI continues to grow and expand, it’s exciting to see what new developments will emerge. But it’s also clear that companies like Google must continue to innovate and adapt if they want to stay ahead in this increasingly competitive and challenging space.


  1. What is PaLM 2, and how does it compare to GPT-4?
  2. What techniques are used in PaLM 2 to achieve better results?
  3. How important is the ability of large models to reason in language modeling?
  4. What impact has AI had on various industries, and how is it evolving to bring improvements?
  5. What must companies like Google do to remain ahead in a competitive AI space?

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