3 citations · 6 across the 4 of their papers we have counts for
4 papers
Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs
CodeArts Model Team, Yang Ye, Jingyuan Tan +24
Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" h…
Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset
Chongjian Yue, Xinrun Xu, Xiaojun Ma +5
Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text,…
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
Chongjian Yue, Xinrun Xu, Xiaojun Ma +6
Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text,…
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
Chongjian Yue, Lun Du, Qiang Fu +4
With the development of temporal networks such as E-commerce networks and social networks, the issue of temporal link prediction has attracted increasing attention in recent years.…