2 citations · 3 across the 5 of their papers we have counts for
5 papers
SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving
Chaofan Tao, Jierun Chen, Yuxin Jiang +11
We present SWE-Lego, a supervised fine-tuning (SFT) recipe designed to achieve state-ofthe-art performance in software engineering (SWE) issue resolving. In contrast to prevalent m…
Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Zhiming Mao, Haoli Bai, Lu Hou +4
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and r…
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation
Zhiming Mao, Huimin Wang, Yiming Du +1
Prior study has shown that pretrained language models (PLM) can boost the performance of text-based recommendation. In contrast to previous works that either use PLM to encode user…
DIGAT: Modeling News Recommendation with Dual-Graph Interaction
Zhiming Mao, Jian Li, Hongru Wang +2
News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations.…
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
Zhiming Mao, Xingshan Zeng, Kam-Fai Wong
Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective represen…