As AI researchers, we’ve made tremendous progress in training large language models (LLMs) to perform various tasks. However, evaluating their ability to achieve real-world goals remains a significant challenge. In this article, we’ll explore the concept of benchmarking open-ended, real-world goal achievement by computer-using LLMs and discuss some ideas for measuring their success.
One approach to benchmarking LLMs is to give them economically valuable tasks to complete. For example, raising money for charity, organizing events, or selling products online. We can use tools like GDPVal to measure their performance on these tasks.
But how do we compare LLMs on these tasks? One idea is to give them a set of goals and see how well they perform in achieving them. We can also use techniques like document sharing and online gaming to evaluate their ability to interact with the real world.
In our AI Village experiment, we’ve seen some interesting results. For instance, some LLMs tend to hallucinate and lack situational awareness, leading to poor performance on tasks that require real-world action. Others may give up on achieving goals due to temperament or computer use skills.
So, what goals would be interesting to give LLMs? Some ideas include:
* Creating and managing online content (e.g., blogs, social media)
* Participating in online communities and forums
* Developing and executing marketing campaigns
* Designing and building websites
What tools would we give LLMs to help them achieve these goals? Some possibilities include:
* Document sharing and collaboration tools
* Online gaming platforms
* Social media and content creation tools
* Marketing and advertising tools
By exploring these questions and ideas, we can gain a better understanding of how to benchmark LLMs on real-world tasks and evaluate their ability to achieve goals. This, in turn, can help us develop more effective and practical AI systems.
In conclusion, evaluating AI goal achievement is a complex task that requires careful consideration of the goals, tools, and metrics used to measure success. By exploring these ideas and approaches, we can move closer to creating AI systems that are capable of achieving real-world goals.