Leveraging Tools: A Paradigm Shift in Language Model Capabilities

In recent advancements within the Natural Language Processing (NLP) field, integrating external tools has emerged as a pivotal paradigm, significantly augmenting the capabilities of Language Models (LMs). This paradigm, exemplified by the basic tool-use model as introduced by Toolformer (Schick et al., 2023), has reshaped how LMs interact with users and process information. Tools are instrumental in facilitating task-solving across various domains due to their diverse functionalities, which can be broadly categorized into perception, action, and computation. Understanding the roles of tools within each category provides insights into their utility:

1. Perception: Perception tools play a crucial role in providing or collecting information from the environment. Language models (LMs) can access real-time data or gather relevant information beyond their intrinsic knowledge by leveraging perception tools. For instance, utilizing a “get time()” API enables an LM to obtain the current time, a piece of information not inherently available within its trained parameters. This ability to gather real-time or external data enhances the LM’s contextual understanding and enables more accurate and relevant responses to user queries.

2. Action: Action tools empower LMs to exert changes in the environment and alter its state. By utilizing action tools, LMs can actively manipulate external entities or systems, thereby enabling dynamic interactions and responses. For example, executing commands like “turn_left()” can direct an embodied agent’s movement in a virtual environment, while functions like “makepost(website, post)” can modify the content of a website. Through action tools, LMs can engage in dynamic, real-time interactions with the environment, enabling adaptive behaviour and task execution.

3. Computation: Computation tools harness the power of programs to address complex computational tasks. These tools, while not directly interacting with the external environment, enable LMs to perform intricate calculations or operations beyond their inherent capabilities. For instance, a calculator is a mathematical calculation computation tool, enhancing the LM’s numerical reasoning abilities. Moreover, computation tools extend beyond numerical tasks, encompassing functions like language translation. By leveraging computation tools, LMs can tackle a wide range of complex tasks efficiently and accurately.

The Basic Tool-Use Paradigm: At its core, the basic tool-use paradigm revolves around the seamless integration of external tools into LM workflows. Consider the scenario where an LM receives a user query such as “How is the weather today?” The LM, operating primarily in natural language, dynamically generates text or tool calls. Upon encountering tasks requiring external assistance, such as obtaining real-time weather information, the LM constructs tool-calling expressions, triggering the execution of the corresponding tools hosted on remote servers. For instance, issuing a “check_weather()” call to a weather server results in receiving the output “sunny”. This output seamlessly replaces the tool call in the LM-generated tokens, facilitating further text generation to formulate a coherent response to the user.

Utilizing Tools: To enable LMs to effectively utilize this tool-use paradigm, contemporary approaches leverage both inference-time prompting and training-time learning methods. Inference-time prompting entails furnishing LMs with task-specific instructions, example pairs, and tool documentation, enabling them to acquire tool-utilization skills contextually. Conversely, learning by training involves exposing LMs to examples utilising various tools during the training phase, either through manual annotation, synthesis by larger LMs, or bootstrapping by the LM itself.

Scenarios for Tool Utilization: The integration of tools vastly expands the horizons of LM applications, particularly in scenarios where human-created, application-specific tools offer substantial benefits. These scenarios encompass diverse domains, including knowledge access, computation activities, interaction with the world, and non-textual modalities. For instance, tools facilitate access to structured knowledge bases, aid in complex computation activities, enable interaction with real-world data such as weather information or geographical locations, and even extend to non-textual modalities like images and audio. The main use cases of the tool utilization are -

1. Knowledge Access Visualization: Create an infographic-style image showcasing various tools used for knowledge access, such as SQL executors, search engines, and retrieval-augmented generation systems. Each tool could be represented visually along with brief descriptions to illustrate their function in enhancing LM knowledge retrieval.
2. Computation Activities Illustration: Design an image depicting different computational tasks being performed with the aid of tools, ranging from math calculations to more complex professional jobs like financial analysis or medical research. Use visual elements such as calculators, Python code snippets, and business tools to convey the diversity of computation activities supported by external tools.
3. Interaction with the World Graphic: Craft a visual representation illustrating how LMs interact with the world through various tools. Include scenes depicting LMs accessing real-time information like weather forecasts, managing calendars and emails, and engaging in physical activities in virtual or real-world environments. Incorporate visual cues such as weather icons, calendar interfaces, and virtual landscapes to convey the breadth of interactions facilitated by external tools.
4. Non-textual Modalities Image: Design an image highlighting the expansion of LM capabilities beyond textual data through access to non-textual modalities. Showcase examples such as LMs accessing images via APIs, playing songs from music platforms, and answering questions about multimedia content. Incorporate visual elements like images, music notes, and speech bubbles to emphasize the multimodal nature of LM interactions.
5. Specialized LM Integration Visual: Create an illustration demonstrating the integration of specialized LMs as tools within the broader LM framework. Show how these specialized models, such as QA models or machine translation models, collaborate with the main LM to enhance its capabilities for specific tasks. Use visual cues such as interconnected neural networks or collaborative workspaces to depict the synergy between different LM models.

Examples

Limitations and Future Directions: While tools exhibit tremendous potential in enhancing LM capabilities, certain tasks, notably machine translation, summarisation, and sentiment analysis, currently demonstrate limited efficacy with tool integration. This stems from the fact that existing tools, often neural networks themselves, fail to offer substantial advantages over the base LM. In such cases, the inherent robustness and versatility of advanced LMs like GPT-4 often overshadow the utility of specialized tool-based approaches.

Conclusion: The integration of external tools marks a paradigm shift in the landscape of NLP, empowering LMs to tackle a broader array of tasks with enhanced efficiency and efficacy. As researchers continue to explore novel methodologies and refine existing techniques, the synergy between LMs and external tools promises to catalyse transformative advancements in natural language understanding and generation.

Through the adoption of innovative methodologies and the continual refinement of existing techniques, the symbiotic relationship between LMs and external tools holds immense promise for advancing natural language understanding and generation, heralding a new era of unprecedented possibilities in NLP.