In recent overviews, we have explored the utility of augmenting large language models (LLMs) with external tools. These models can be taught to leverage tools in a variety of ways. However, we should realize that existing tool-following LLMs leverage only a limited set of potential tools [3], whereas the range of problems we want to solve with LLMs is nearly endless! With this in mind, it becomes clear that such a paradigm is limiting — we will always be able to find scenarios that require tools that do not yet exist. In this overview, we will explore recent research that aims to solve this problem by providing LLMs with the skills to create their own tools. Such an approach draws an interesting analogy to human life, as the ability to fabricate tools led to major technological advancements. Now, we explore the impact of similar techniques upon the evolution of LLMs.
“According to the lessons learned from the evolutionary milestones of humans, a crucial turning point was that humans got the ability to fabricate their own tools to address emerging challenges. We embark on an initial exploration to apply this evolutionary concept to the realm of LLMs.” — from [1]
Prior to learning more about tool-making LLMs, there are a few background concepts that we need to refresh. We have covered many of these ideas in recent overviews, but we’ll briefly go over them again now to make our discussion of recent publications more comprehensive and understandable.
Why should we use tools?
In prior overviews, we have learned about a few different kinds of tools that can be integrated with LLMs to improve their performance, such as:
- Basic Tools (calculators, search engines, etc.) https://towardsdatascience.com/can-language-models-make-their-own-tools-cbc7c3777d22?source=rss—-7f60cf5620c9—4
- Deep Learning Model APIs https://towardsdatascience.com/can-language-models-make-their-own-tools-cbc7c3777d22?source=rss—-7f60cf5620c9—4
