4 papers
ACEBench: Who Wins the Match Point in Tool Usage?
Chen Chen, Xinlong Hao, Weiwen Liu +13
Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
Xu Huang, Weiwen Liu, Xingshan Zeng +8
The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capa…
GUI Agents with Foundation Models: A Comprehensive Survey
Shuai Wang, Weiwen Liu, Jingxuan Chen +12
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have facilitated the development of intelligent agents…