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ToolEmu: An AI Framework for Emulating Tool Execution and Testing Language Model Agents

Assessing risks of language models in real-world scenarios is crucial to their safe deployment. ToolEmu offers a framework for emulation and evaluation, unveiling potential failures and quantifying associated risks. LM agents still require further refinement for enhanced safety.

by Lilu Anderson
Fri, Jan 26, 2024 12:30am
in Artificial Intelligence
• 3 mins read
14.2k
ToolEmu: An AI Framework for Emulating Tool Execution and Testing Language Model Agents | FinOracle

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Assessing Risks of Language Models in Real-World Scenarios

Recent advancements in language models (LMs) and their usage in various tools have paved the way for semi-autonomous agents that operate in real-world scenarios. While these agents bring about exciting possibilities and enhanced capabilities, they also pose significant risks if not properly managed. Failures to follow instructions could lead to serious consequences such as financial losses, property damage, and even life-threatening situations. It is crucial, therefore, to thoroughly assess and identify any potential risks associated with these language models before deploying them.

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