Tool Learning with Foundation Models (2304.08354v3)

Abstract: Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. In general, we hope this paper could inspire future research in integrating tools with foundation models.

• The paper introduces a comprehensive framework for tool learning using FMs that integrates a tool set, environment, controller, and perceiver.
• The paper demonstrates how foundation models employ few-shot and zero-shot learning to align user intent with specialized tool functionalities.
• The paper presents empirical evaluations across diverse domains, highlighting both the potential and challenges of generalizable tool learning systems.

Tool Learning with Foundation Models: A Comprehensive Review

The paper "Tool Learning with Foundation Models" systematically investigates the emerging paradigm of tool learning enabled by foundation models (FMs) and provides a comprehensive review of existing literature, methodologies, and future research directions. The authors explore both the technical developments and the underlying cognitive connections of tool learning, while presenting an evaluative framework that encompasses the various components involved in this process.

Key Concepts and Framework

At the core of this paper is the idea that with the advent of powerful FMs, AI systems have the potential to creatively tackle problems and devise solutions by leveraging and integrating specialized tools, much like humans do. The paper defines a comprehensive framework for tool learning that involves four key components: the tool set, the environment, the controller, and the perceiver.

1. Tool Set: This includes a variety of specialized tools, each serving different functionalities and input/output modalities, from simple APIs to complex systems like embodied virtual environments.
2. Environment: Both virtual and real environments are considered to provide feedback about tool executions. The environment interacts dynamically with the controller through direct bindings and influences subsequent actions.
3. Controller: Typically an FM, the controller manages the planning and execution of tasks, aligning user instructions with available tools to achieve desired outcomes. This includes understanding user intents and reasoning through complex, multi-step processes.
4. Perceiver: This component is responsible for processing feedback from the user and environment, facilitating the decision-making process in the controller by summarizing observations into a coherent format.

Key Challenges and Methodologies

The paper highlights several challenges within the context of tool learning and reviews methodologies to address them:

• Understanding Intent and Tools: Bridging the gap between user intent and tool functionalities is crucial for effective planning and execution. The authors discuss leveraging few-shot and zero-shot learning capabilities of FMs through prompt engineering.
• Planning with Reasoning: Foundation models are tasked with decomposing complex problems and generating plans using both introspective (internal logic) and extrospective (dynamic interaction-based) reasoning strategies.
• Generalizable Tool Learning: To enable FMs to seamlessly adapt to a wide array of tools and functionalities, the authors discuss strategies like interface unification, meta-learning, and curriculum learning to foster generalization across different tools.
• Model Training and Feedback Learning: They explore training strategies such as learning from demonstrations, self-supervision, and reinforcement learning, focusing on interaction feedback from the environment or human users.

Empirical Evaluation

Experiments with various tools, spanning domains like machine translation, calculators, maps, and more, illustrate the potential and limitations of tool learning with current FMs. These evaluations reveal that FMs can effectively leverage tools given appropriately engineered prompts, leading to improved performance on complex tasks.

Discussion and Future Prospects

The authors recognize potential implications and explore several open problems such as ensuring safety, trustworthiness, and governance of tool-empowered AI systems. They also broach discussions on the competitive and complementary roles of human and AI collaboration, issues in personalized tool learning, and the possibility of FMs assuming roles as tool creators rather than mere users.

Overall, the paper serves as a thorough roadmap for researchers interested in understanding and advancing the field of tool learning with foundation models. With its extensive analysis and discussion, it paves the way for future explorations aimed at harnessing this paradigm to develop ever more intelligent and adaptable AI systems.