The Role of Tools in Language Models

Tools are programs that help language models (LMs) perform tasks better. LMs are good at generating text but sometimes struggle with complex tasks or information they haven't learned about. By using tools, LMs can complete tasks that require skills beyond their basic functions.

#Why Do LMs Need Tools?

Language models can answer questions based on the text they were trained on. For example, they can respond to "What is the capital of the US?" with "Washington D.C." However, LMs find it hard to do things like complex math or gather real-time information, such as current weather or date, since this data was not available during training.

To help with these challenges, researchers are looking at ways to enhance LMs using tools. For instance, a calculator helps with math, and an API (Application Programming Interface) can fetch current information from the web. Tools add new abilities to LMs, allowing them to perform tasks they could not manage before, providing better answers and completing tasks more effectively.

#Defining Tools

To clarify, tools in this context refer to external programs that LMs can use. These tools are not part of the LM itself; rather, they come from a different source and can be applied to various tasks. A tool can be thought of as a function that takes some input and gives an output.

Based on this understanding, we can categorize tools into three main types:

1. Perception Tools: These collect or provide information from the environment. For example, a tool that gives the current time.

2. Action Tools: These perform actions in the environment. For example, a tool that can change a website's content.

3. Computation Tools: These assist with complex calculations or operations. For instance, a calculator is a computation tool for math problems, and a translator is a computation tool that helps translate text between languages.

Some tools can fit into more than one category. A search engine, for example, can retrieve information (perception) and measure the relevance of documents (computation).

#How Do Tools Help LMs?

Tools improve LMs in several ways:

• They provide access to real-time information that LMs cannot store during training.
• They allow LMs to perform tasks that require actions in the real world.
• They assist LMs in complex calculations or reasoning tasks.

By using these tools, LMs can accomplish more and provide better responses to users.

#Tool Use in Language Models

When LMs want to use a tool, they follow a basic process. Let's say a user asks, "How is the weather today?" The LM may start to generate a text response but realizes it needs current information. The LM will then create a command for a weather-checking tool, fetch the current weather, replace the tool command in its text, and finish a complete response, like "It is sunny today."

To help LMs learn to use tools, researchers use two main methods:

• Inference-time prompting: This involves giving LMs examples and instructions about tool use during their operation.

• Training-time learning: This method teaches LMs using examples during their training phase.

#Types of Tools and Scenarios

Numerous tools exist that LMs can use in specific scenarios. Here are some examples:

#1. Knowledge Access

Sometimes, LMs cannot remember everything they need to know. To help with this, tools like SQL executors can access databases. A search engine tool can enable LMs to pull up-to-date information from the web.

#2. Complex Computation

For tasks like math calculations, LMs can struggle without help. Tools such as calculators or more complex programming tools can enhance their ability to compute.

#3. Interaction with the World

LMs, by themselves, cannot perceive or act in the real world. With tools, they can gather real-time information, manage emails or calendars, or even interact within digital environments like games.

#4. Non-Textual Data

Some LMs can only work with text, but others can access images or audio. Tools can help these LMs understand and process non-text data.

#5. Specialized Language Models

Researchers have also developed specialized LMs that can be used as tools. The main LM can plan tasks and delegate specific requests to these specialized models to improve performance.

#Where Tools Might Not Help

While tools can enhance many tasks, they are not always beneficial. Some tasks, like machine translation or summarization, can often be completed well by LMs without tools. The tools available for these tasks usually do not provide significant advantages compared to the main LMs, which may already perform well.

#Advanced Methods for Tool Use

As research continues, new methods for better tool usage are being developed. Here are a few advanced techniques:

#1. Complex Tool Selection

When multiple tools are available, LMs may need to decide which to use. If only a few tools exist, LMs can select them directly. However, if there are many tools, a retrieval system can help narrow down the most appropriate options.

#2. Programmatic Contexts

In programming tasks, tools can function as building blocks in code. LMs can generate programs that combine these tools, providing solutions to more complex tasks.

#3. Creating Tools

When specific tools are unavailable, researchers are now looking into methods for LMs to create their own tools. This could involve designing new functions or programming languages to solve particular tasks.

#Evaluating Tool Use

To understand how well tools enhance LMs, various benchmarks are used to test their effectiveness. Some possible evaluation metrics include:

• Task Completion: This checks if tools help LMs complete their assigned tasks.

• Tool Selection Accuracy: Involves measuring how well LMs choose the correct tools during a given task.

• Tool Reusability: Evaluates if tools can be used multiple times for different tasks.

#Desired Properties for Tools

To improve tools for LMs, additional properties should be considered:

• Efficiency: Tools should not slow down LMs significantly when used.

• Quality: Tools should perform well and not fail unexpectedly.

• Reliability: Tools need to provide consistent results, particularly ones involving neural models.

• Reproducibility: Since some tools may return different results over time, a method for executing tests that ensures consistent results would be beneficial.

• Safety: Users should trust the tools they use, meaning they need to be straightforward and secure.

#Trade-offs in Tool Usage

While tools often improve performance, there is a cost associated with using them. The benefits must be balanced against the extra computation required to train LMs to use tools effectively.

Some tasks see a significant increase in performance from tool usage, while others might not benefit as much. For example, using tools for math problems can greatly enhance performance, while some language tasks may not see a marked improvement.

#Conclusion

In summary, tools are vital for extending what language models can do. They help LMs perform a broader range of tasks by providing access to real-time information, enabling complex computations, and allowing interaction with the world. Understanding how LMs use tools can guide future research, allowing for more sophisticated models and tools that work together effectively.

As researchers continue to explore this area, we can expect new benchmarks and evaluation metrics that will refine our understanding of LM tooling. The future holds promising potential for integrating tools, enhancing the capabilities of language models, and ultimately improving user experiences.