2 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning
Yimin Deng, Zhenxi Lin, Yejing Wang +9
Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capabilities in commonsense reasoni…
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
Yimin Deng, Yejing Wang, Zhenxi Lin +8
Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited…
Chinese-SkillSpan: A Span-Level Dataset for ESCO-Aligned Competency Extraction from Chinese Job Ads
Guojing Li, Zichuan Fu, Junyi Li +6
Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-mark…
Job Skill Extraction via LLM-Centric Multi-Module Framework
Guojing Li, Zichuan Fu, Junyi Li +8
Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (LLMs) often yield malformed s…
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Yimin Deng, Yuxia Wu, Yejing Wang +9
Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on eith…
Training-free LLM Merging for Multi-task Learning
Zichuan Fu, Xian Wu, Yejing Wang +6
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen…
Sliding Window Attention Training for Efficient Large Language Models
Zichuan Fu, Wentao Song, Yejing Wang +7
Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity…