11 citations · 38 across the 7 of their papers we have counts for
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cs.CL2024★ 10 cited
Executable Code Actions Elicit Better LLM Agents
Xingyao Wang, Yangyi Chen, Lifan Yuan +4
Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challe…
cs.CL2023
Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge
Genglin Liu, Xingyao Wang, Lifan Yuan +2
Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systema…
cs.CL2023★ 8 cited
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Lifan Yuan, Yangyi Chen, Xingyao Wang +3
Large language models (LLMs) are often augmented with tools to solve complex tasks. By generating code snippets and executing them through task-specific Application Programming Int…