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20232026
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 123 across the 18 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.SE2023★ 3 cited

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub

Bohan Lyu, Xin Cong, Heyang Yu +9

Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. Whi…

cs.RO2023★ 11 cited

ProAgent: From Robotic Process Automation to Agentic Process Automation

Yining Ye, Xin Cong, Shizuo Tian +9

From ancient water wheels to robotic process automation (RPA), automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles…

cs.CL2023

AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

Weize Chen, Yusheng Su, Jingwei Zuo +13

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-…

cs.AI2023★ 72 cited

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Yujia Qin, Shihao Liang, Yining Ye +16

Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to…

cs.CL2023★ 33 cited

Tool Learning with Foundation Models

Yujia Qin, Shengding Hu, Yankai Lin +38

Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models,…