It's worthwhile to know what the best-performing AI language models are and how they compare since Perceptrader AI EA uses large language models to help with market analysis.
This topic deserves a closer look.
Different AI model LLMs can be compared to each other and measured in several ways. The LLM performance can be evaluated using a variety of methods, thanks to a resource made by professional computer scientists. Three different ways of measuring performance are presented in a simple table.
Scores are based on human evaluations in the first column. People can compare different LLM answers on a separate website. A score is then calculated from these comparisons.
Similar to the first score column, the second score column uses the best-performing LLM, GPT-4. Because of this, it is able to evaluate many answers without requiring human intervention. It is interesting to note that the results are very similar to those of human evaluations, as described in this scientific article. GPT-4 is biased when measuring its own responses, even though it obviously prefers its own answers.
In the third score column, you will find a variety of questions covering a variety of topics. A wide variety of questions can be found here, including elementary mathematics, US history, computer science, law, and so on. GPT-3 was the first LLM evaluated with it in 2020.
From this, what can we learn?
In simple terms, GPT-4 performs the best among large language models, no matter how it is measured. So Perceptrader AI uses it as its base model. Claude, the second-best model, comes from OpenAI rival Anthropic. GPT-3.5 and all its competitors are outperformed, despite that Claude provides a larger context window, up to 100k tokens (about 75000 words). This is your LLM if you want to sum up a book.
In comparison to all its competitors, Bard, which is based on the PaLM model, trails behind all of them. Since it has internet access, it can be a better choice in some scenarios since it doesn't always need to be updated with current data. GPT-4 is unlikely to be overtaken by the next Google LLM.
In Conclusion
Claude and Bard are among other contenders that have unique features that are suitable for different needs, particularly as related to the Perceptrader AI. Furthermore, open-source models, such as the Llama family, can be trained on specific data to outperform their competitors. Because of this, it's crucial to choose an LLM that takes into account both the overall performance and the unique requirements of the task at hand. Moving forward, we can expect the landscape to evolve, with companies such as Google gearing up to challenge current leaders. Any trader or other professional seeking to harness AI power must stay on top of these developments.
Aside from her love for algorithmic trading software, Valeriia Mishchenko, the Perceptrader AI developer is fascinated by the latest advancements in AI technologies. She claims that researching this area takes up most of her time. She'd be happy to share more insights with her clients in the future if they are interested.
