Meet ToolEmu: An Artificial Intelligence Framework that Uses a Language Model to Emulate Tool Execution and Enables the Testing of Language Model Agents Against a Diverse Range of Tools and Scenarios Without Manual Instantiation

Recent advancements in language models have led to the development of semi-autonomous agents like WebGPT, AutoGPT, and ChatGPT plugins for real-world use. However, the transition from text interactions to real-world actions brings risks. To address this, a new framework called ToolEmu utilizes language models to simulate tool executions and evaluate risks, aiming to enhance agent safety.

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Recent Advances in Language Models and Tools

Recent advancements in language models (LMs) and tool usage have led to the development of semi-autonomous agents like WebGPT, AutoGPT, and ChatGPT plugins that operate in real-world scenarios. While these agents promise enhanced LM capabilities, there are risks associated with transitioning from text interactions to real-world actions through tools.

Identifying and Mitigating Risks

Recognizing the potential risks of using LM agents in real-world scenarios, it becomes essential to identify and address even low-probability risks before deployment. This is crucial in preventing financial losses, property damage, or life-threatening situations.

Introducing ToolEmu

To address the challenges of testing LM agents, a new framework called ToolEmu has been introduced. ToolEmu is a Language Model LM-based tool emulation framework designed to examine LM agents across various tools, pinpoint realistic failures in diverse scenarios, and aid in developing safer agents through an automatic evaluator.

Key Features of ToolEmu

At the core of ToolEmu is the use of an LM to emulate tools and their execution sandboxes. This enables rapid prototyping of LM agents across scenarios, accommodating high-stakes tools lacking existing APIs or sandbox implementations. Additionally, ToolEmu includes an adversarial emulator for red-teaming, enhancing risk assessment and identifying potential LM agent failure modes.

Scalable Risk Assessments

ToolEmu also features an LM-based safety evaluator that quantifies potential failures and associated risk severities. This automatic evaluator contributes to building a benchmark for quantitative LM agent assessments across diverse tools and scenarios.

Impact and Recommendations

The emulators and evaluators in ToolEmu contribute to the development of a benchmark for quantitative LM agent assessments, highlighting the need for continued efforts to enhance LM agent safety.

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