3 papers
cs.CL2025
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…
cs.CL2025
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…
cs.CL2025
Advancing and Benchmarking Personalized Tool Invocation for LLMs
Xu Huang, Yuefeng Huang, Weiwen Liu +5
Tool invocation is a crucial mechanism for extending the capabilities of Large Language Models (LLMs) and has recently garnered significant attention. It enables LLMs to solve comp…