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20232025
most citedToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

72 citations · 240 across the 9 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV2023★ 4 cited

Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants

Tianyu Yu, Jinyi Hu, Yuan Yao +10

Recent Multimodal Large Language Models (MLLMs) exhibit impressive abilities to perceive images and follow open-ended instructions. The capabilities of MLLMs depend on two crucial…

cs.CL2023★ 13 cited

Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages

Jinyi Hu, Yuan Yao, Chongyi Wang +13

Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English…

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.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,…