5 citations · 12 across the 7 of their papers we have counts for
7 papers
Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models
Junhao Liu, Jian-Wei Zhang, Tao Huang +3
Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial inst…
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs
Lei Zhang, Yunshui Li, Jiaming Li +7
Some recently developed code large language models (Code LLMs) have been pre-trained on repository-level code data (Repo-Code LLMs), enabling these models to recognize repository s…
One-Shot Learning as Instruction Data Prospector for Large Language Models
Yunshui Li, Binyuan Hui, Xiaobo Xia +9
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may comp…
Marathon: A Race Through the Realm of Long Context with Large Language Models
Lei Zhang, Yunshui Li, Ziqiang Liu +5
With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models' c…
Self-Distillation with Meta Learning for Knowledge Graph Completion
Yunshui Li, Junhao Liu, Chengming Li +1
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddin…
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Junhao Liu, Linjun Shou, Jian Pei +3
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi,…
Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering
Jian Wang, Junhao Liu, Wei Bi +4
Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aw…