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Thomas Carlyle, a 19th-century British philosopher, believed that the human ability to use tools played a crucial role in shaping human development.
He expressed this idea as: “Man is a tool-using animal. Without tools, he is nothing; with tools, he is all.”
As we delve into the era of artificial intelligence, Carlyle’s philosophy emerges as a potential remedy for addressing certain notable limitations inherent in large language models (LLMs).
The Limitations of Large Language Models (LLMs)
While LLMs such as ChatGPT and Google Bard have made impressive strides in areas like natural dialogue, mathematical reasoning, and program synthesis, their core limitation lies in their fixed capacity to absorb and store information.
This lack of flexibility hampers their ability to adapt to changing contexts, and the confined operational context adds challenges in keeping up with a world that’s constantly changing. As a result, LLMs need regular retraining to update their knowledge and improve their reasoning abilities.
Can we teach LLMs to use tools? If so, that opens doors to extensive and evolving knowledge bases, enabling them to tackle intricate computational tasks.
Demonstrated through access to search technologies and databases, we can enhance LLMs to engage with a considerably broader and more dynamic knowledge space.
Likewise, by enabling access to computational tools, LLMs prove their capability to handle intricate computational tasks. As a result, major LLM providers are now incorporating plugins that permit LLMs to seamlessly leverage external tools via APIs.
Creative Capacity Beyond Tool Usage
As LLMs showcase an impressive ability to employ tools, it becomes crucial to recognize the fundamental aspect of Carlyle’s philosophy that extends beyond mere tool usage.
It underscores our distinct capability to use tools and ingeniously craft new ones to address emerging challenges.
This creative capacity, involving the employment of existing tools to forge new ones, stands as the genuine driving force behind human development.
Beyond the realm of physical tools, we also exhibit a natural inclination to construct mental tools or cognitive strategies to navigate the repetitive tasks in our daily lives.
Consider the analogy of learning to drive a car: initially, we conscientiously perform tasks, but with mastery, driving becomes second nature, requiring minimal cognitive effort.
Dual-Process Theory and LLM Capabilities
The dual-process mind theory offers insights into how our minds operate by dividing human cognitive abilities into two systems: System 1 and 2.
System 1, akin to a collection of repetitive processes, facilitates fast, automatic, and intuitive thinking, responsible for quick judgments and heuristics.
On the other hand, System 2 embodies a more deliberate, analytical approach. It engages in reasoning, critical thinking, and intricate cognitive processes when faced with complex problems or decisions, showcasing logical reasoning and heightened cognitive control.
Interestingly, the System 2 of the dual-process framework resonates with the evolving capabilities of LLMs to perform reasoning and critical thinking.
However, like our cognitive processes, LLMs (similar to System 2) need to develop mechanisms to produce System 1-like automated repetitive responses.
LLMs as Tool Makers (LATM)
In the ongoing effort to empower LLMs with the capacity to create new tools, researchers from UC Berkeley and Microsoft embark on this journey with the development of a framework they term ‘LLMs As Tool Makers (LATM)‘ that enables LLMs to generate their own reusable tools to tackle new tasks.
LATM employs a two-phase system to enable LLMs to efficiently create and utilize task-specific tools, providing a comprehensive approach to its functionality.
In the first phase, known as tool creation, an LLM assumes the role of the “tool builder.” Leveraging its natural language processing capabilities, it crafts Python functions tailored to solve distinct problems.
This unique skill enhances the problem-solving proficiency of LLMs, introducing a new dimension of capability.
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Subsequently, in the tool application phase, another LLM, termed the “tool user,” applies these custom-built tools to address new requests and handle intricate tasks.
These pre-constructed tools enable LLMs to navigate various problem domains efficiently without incurring unnecessary computational overhead.
An interesting aspect is that the tools created can be repurposed for repetitive tasks within a workflow or adapted for new tasks as needed. This adaptability contributes to the construction of scalable and cost-effective LLMs capable of handling complex tasks.
In their empirical study, the research team applied LATM to solve intricate reasoning problems, including challenging tasks such as Big-Bench tasks. The experiments demonstrated that LATM achieved performance levels comparable to resource-intensive models like GPT-4, all while significantly reducing computation costs.
Challenges and Future Directions
This endeavor presents a promising avenue for enhancing the advanced problem-solving capabilities of LLMs by granting them the ability to construct tools tailored for specific tasks autonomously.
However, it marks a foundational step in empowering language models to innovate and devise new tools to meet emerging challenges.
Furthermore, instead of being confined to a single programming language and basic functionalities, LLMs should be adaptable across multiple languages, each with unique capabilities suited for addressing diverse challenges and automating more sophisticated repetitive tasks, such as driving.
A captivating prospect for future research involves enabling the tool maker to refine and upgrade existing tools to handle new problem instances, mirroring the iterative process seen in software development.
This adaptability can potentially accelerate the evolution of the AI ecosystem, presenting a myriad of opportunities. These capabilities collectively empower LLMs, fostering a sense of self-reliance in their problem-solving abilities.
The Bottom Line
Inspired by Carlyle’s tool philosophy, LATM offers a human-like approach to enhancing LLMs. LATM enables LLMs to efficiently craft and use task-specific tools, showcasing adaptability and cost-effectiveness.
As LLMs evolve, the dual-process theory guides their transition between analytical reasoning and automated responses. LATM’s two-phase system demonstrates success in solving complex problems, competing with resource-intensive models while significantly reducing costs.
Looking forward, challenges include expanding LLM adaptability across multiple languages and refining tools iteratively.
This adaptability propels the evolution of the AI ecosystem, offering exciting possibilities. These advancements empower LLMs, instilling a sense of self-reliance in their problem-solving capabilities.
