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

72 citations · 211 across the 32 of their papers we have counts for

collaborators
Showing 2023Show all

7 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★ 2 cited

Rational Decision-Making Agent with Internalized Utility Judgment

Yining Ye, Xin Cong, Shizuo Tian +5

Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step…

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

Exploring Format Consistency for Instruction Tuning

Shihao Liang, Runchu Tian, Kunlun Zhu +6

Instruction tuning has emerged as a promising approach to enhancing large language models in following human instructions. It is shown that increasing the diversity and number of i…

cs.SE2023★ 71 cited

ChatDev: Communicative Agents for Software Development

Chen Qian, Wei Liu, Hongzhang Liu +11

Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a…

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,…