1 citations · 1 across the 5 of their papers we have counts for
5 papers
SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs
Hongfei Xia, Hongru Wang, Zeming Liu +3
Large Language Models (LLMs) have exhibited great performance in autonomously calling various tools in external environments, leading to better problem solving and task automation…
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
Jingjing Liu, Zeming Liu, Zihao Cheng +7
Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair, which may substantially reduce the time consumption o…
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices
Silin Li, Yuhang Guo, Jiashu Yao +2
Large language models (LLMs) have the potential to revolutionize smart home assistants by enhancing their ability to accurately understand user needs and respond appropriately, whi…
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments
Yuheng Lu, Qian Yu, Hongru Wang +7
Graphical User Interface (GUI) agents, which autonomously operate on digital interfaces through natural language instructions, hold transformative potential for accessibility, auto…
ToolSpectrum : Towards Personalized Tool Utilization for Large Language Models
Zihao Cheng, Hongru Wang, Zeming Liu +4
While integrating external tools into large language models (LLMs) enhances their ability to access real-time information and domain-specific services, existing approaches focus na…