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20232026
most citedToolACE: Winning the Points of LLM Function Calling

3 citations · 3 across the 22 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

Ensuring Consistency for In-Image Translation

Chengpeng Fu, Xiaocheng Feng, Yichong Huang +9

The in-image machine translation task involves translating text embedded within images, with the translated results presented in image format. While this task has numerous applicat…

cs.CV2024

Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective

Xinmiao Yu, Xiaocheng Feng, Yun Li +10

Recent Large Vision-Language Models (LVLMs) have shown promising reasoning capabilities on text-rich images from charts, tables, and documents. However, the abundant text within su…

cs.CL2024

Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges

Yangfan Ye, Xiaocheng Feng, Xiachong Feng +11

Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data.…

cs.LG2024★ 3 cited

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…

cs.CL2024

Learning Fine-Grained Grounded Citations for Attributed Large Language Models

Lei Huang, Xiaocheng Feng, Weitao Ma +8

Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with…

cs.CL2024

Concise and Precise Context Compression for Tool-Using Language Models

Yang Xu, Yunlong Feng, Honglin Mu +9

Through reading the documentation in the context, tool-using language models can dynamically extend their capability using external tools. The cost is that we have to input lengthy…