ToolSandbox LLM Tool-Use Benchmark released by Apple: A conversational and interactive assessment benchmark for LLM tool-use skills

Modern Large Language Models (LLMs) are increasingly designed as autonomous agents that can interact with the real world through perception, decision-making and action. An important issue in this area is whether these models can effectively use external tools. The use of tools in LLMs includes:

Recognize when a tool is needed.
Choosing the right tools.
Perform actions that accomplish these tasks.

Some of the key issues that need to be addressed to move LLMs beyond previous milestones relate to accurately assessing their capabilities for tool deployment in a real-world environment. The standard evaluation benchmarks for most of these systems address, at best, static single-turn settings, that is, situations that do not require stateful multi-turn responses where the model must retain details of previous interactions and context changes. The lack of comprehensive evaluation frameworks means that it can be difficult to assess how effectively such models can perform tasks that require external tools, particularly in dynamic and interactive environments where actions performed by the model can have cascading effects on the state of the world.

To measure the capabilities of LLM tools, several evaluation benchmark collections have been developed, such as BFCL, ToolEval, and API-Bank. These benchmarks are designed to evaluate the models' capabilities to interact with web services in combination with function invocation scenarios. However, the benchmarks have several limitations. One of them is that both BFCL and ToolEval work with stateless interactions. That is, the model's actions do not modify the environment. Second, while API-Bank includes stateful tools, it also needs to adequately investigate the impact of state dependencies on the execution of initiated tasks. These limitations lead to an incomplete understanding of how well LLMs can handle complex, real-world tasks that involve multiple steps and environment interactions.

The Apple research team addressed these challenges by introducing a new evaluation metric: ToolSandbox serves to evaluate the specific tool usage possibilities of LLMs in stateful and interactive conversation situations. ToolSandbox would enable a much richer evaluation environment that includes stateful tool execution, implicit state dependencies, and policy-based conversational evaluation with a simulated user. This would thus enable an in-depth evaluation of how suitable the LLMs are for real-world and complex tasks that involve many interactions and decisions based on the actual state of an environment.

The ToolSandbox Framework creates a Python-based execution environment where LLMs interact with a simulated user and a set of tools to complete tasks. The world state is captured in the environment and its actions are measured against predefined milestones and minefields in the model. The former consist of critical steps that the model must achieve to complete the task, while the latter consists of an event that the model should not perform. The framework thereby enables continuous adjustment of the evaluation based on the model's performance, enabling analysis of how well the model can adapt to environmental changes and how well it can perform multitasking operations with interconnected steps and dependencies.

The most important innovation that ToolSandbox Alongside the existing benchmarks, it introduces stateful tools that depend on the current state of the world to function as expected. For example, take a messaging tool that sends a message: this only works if the cellular service is on and there may be other prerequisites to consider, such as battery level. It also includes an LLM-based user simulator where interactions with the model are performed in a lifelike, policy-compliant manner, allowing a more realistic assessment of its performance in real-world conditions. In addition, the framework allows for the extension of tool names and descriptions of different scrambling tools, in turn testing the resulting robustness of the model's tool usage capabilities.

The ToolSandbox Benchmark has revealed performance differences between different LLMs and highlighted significant discrepancies between proprietary and open-source models. Proprietary models such as OpenAI's GPT-4o and Anthropic's Claude-3-Opus outperformed other models, achieving higher similarity scores in several use cases. In contrast, open-source models such as Hermes-2-Pro-Mistral-7B struggled with complex tasks involving state dependencies and canonicalization. For example, in a canonicalization task where the model standardizes user input, GPT-4o achieved a similarity score of 73.0, while Hermes-2-Pro-Mistral-7B only achieved 31.4. The benchmark also highlighted challenges related to insufficient information scenarios, where a model must detect the need for the right tool or data to perform a task without generating incorrect tool calls or arguments.

In this respect ToolSandbox represents a notable advance in the benchmarking process of LLM tool usage skills, providing an assessment framework that is more comprehensive and realistic than before. Emphasizing the stateful and interactive nature of the task, ToolSandbox provides numerous valuable insights into understanding the capabilities and limitations of LLMs in real-world applications. The results of this benchmark suggest further work and developments in this direction, especially regarding the robustness and adaptability of LLMs to complex and multi-level interactions that are constantly changing.

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