GPT-4 Outperforms GPT-3.5 Across the Board on a Variety of Study Benchmarks

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In Brief

The GPT-4 has achieved a higher grade threshold than the GPT-3.5 on a variety of benchmarks.

This is a major achievement as it shows that machines are not only capable of human-like intelligence but can also outperform us, which raises questions about the future of AI and its potential impact on the job market.

GPT-4 is significantly outperformed by state-of-the-art (SOTA) models, including those that use additional training protocols or benchmark-specific design, as well as existing big language models.

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The GPT-4 has achieved higher scores than the GPT-3.5 on a variety of benchmarks. This is a major breakthrough for machines as it proves that they can now not only solve problems that are originally designed for but can also do so better than university students.

GPT-4 outperforms GPT-3.5 across the board on a variety of study benchmarks

There are a few things to take into consideration when looking at this result. Firstly, the GPT-4 was not given any specific training for these exams. It proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. Secondly, it is important to note that the GPT-4’s performance may not necessarily reflect the abilities of human test-takers, as it operates on a different set of principles and algorithms.

This is a major achievement as it shows that machines are not only capable of human-like intelligence but can also outperform us. This paves the way for a future where machines can take on more and more complex tasks, ultimately leading to a future in which they can assist us in our everyday lives.

The GPT-4’s ability to outperform humans in certain tasks raises questions about the future of artificial intelligence and its potential impact on the job market. It also highlights the need for continued research and development in this field to ensure that AI is used ethically and responsibly.
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GPT-4, for example, passes a simulated bar exam with a score in the top 10% of test takers; GPT-3.5’s score was in the bottom 10%. This significant improvement in GPT-4’s performance is due to its larger training data and improved architecture. It is expected to have a wide range of applications in various fields, including natural language processing and automated writing.

GPT-4 exhibits human-level performance on the majority of these professional and academic exams. Notably, it passed a simulated version of the Uniform Bar Examination with a score in the top 10% of test takers. The model’s capabilities on exams appear to stem primarily from the pre-training process and are not significantly affected by RLHF. On multiple-choice questions, both the base GPT-4 model and the RLHF model performed equally well on average across the developers of the exam tested.

The majority of state-of-the-art (SOTA) models, including those that may use additional training protocols or benchmark-specific design, as well as existing big language models, are significantly outperformed by GPT-4.

GPT-4’s performance in terms of academic standards. Developers contrast GPT-4 with the best SOTA for an LM-evaluated few-shot as well as the best SOTA with benchmark-specific training. With the exception of DROP, GPT-4 outperforms all current LMs on all benchmarks and SOTA with benchmark-specific training.

Internally, developers have been utilizing GPT-4, which has had a significant impact on activities like programming, sales, support, and content moderation. The second stage of our alignment method is now underway as developers use it to aid humans in reviewing AI outcomes.

MMLU (Massive Multi-Task Language Understanding) dataset contains questions from a very wide range of topics on language understanding in different tasks (spanning 57 domains, including mathematics, biology, law, social and human sciences, etc.). There are four possible answers to the question, one of which is correct. That is, random guessing shows a result of 25% correct answers. See the picture below for examples of questions and their difficulties. The average person-marker (that is, this is not a scientist, not a professor—an ordinary person who moonlights as a markup) answers correctly to  35% of the questions; however, the experts can reach a score of +/- 90%.

Performance of GPT-4 in a range of languages compared to earlier models in English on MMLU. GPT-4 exceeds the English-language performance of existing language models for the large majority of languages examined, including low-resource languages such as Latvian, Welsh, and Swahili.
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Originally, the entire dataset was in English. But what if questions and answers are translated into other languages, especially less common ones? Will the model work for them somehow? In this test, the Microsoft Azure Translate service was used for translation. Translations are not perfect; in some cases, important information is lost. However, even in this case, the GPT-4 performs well in other languages. In the translated versions of the MMLU, GPT-4 outperforms the English level of other large models (including Google’s) by 24 of the 26 languages examined.

What’s more, GPT-4 performs better in rare languages than ChatGPT did in English (ChatGPT achieved a score of 70.1%, while the new model’s score for Thai was 71.8%). The score for the test in English was the highest, with GPT-4 performing 10% better than other models, including the largest PaLM from Google. It achieved a score of 86.4%, while a group of experts—90%.

  • By the summer of 2023, AI might have reached a new level of power thanks to ChatGPT, a chatbot that uses the GPT-4 algorithm and outperforms GPT-3 by a factor of 570. A variety of elements contribute to ChatGPT’s success, including its design to be more “human-like” and its use of cutting-edge data mining and natural language processing to increase its effectiveness and accuracy.
  • Microsoft and OpenAI announced their collaboration renewal and plans for Bing search to adopt AI-enhanced lookup capabilities in January. The very sophisticated GPT3.5 model’s replacement, GPT4, has just been launched, and it has the potential to greatly enhance Bing search’s capacity to comprehend natural language queries and deliver more accurate results. It is a good idea to have a good backup plan in case something goes wrong.

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Any data, text, or other content on this page is provided as general market information and not as investment advice. Past performance is not necessarily an indicator of future results.

Damir Yalalov

Damir is the Editor/SEO/Product Lead at He is most interested in SecureTech, Blockchain, and FinTech startups. Damir earned a bachelor's degree in physics.

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