most citedFrom Beginner to Expert: Modeling Medical Knowledge into General LLMs

4 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning

Congyun Jin, Ming Zhang, Xiaowei Ma +13

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.…

cs.LG2024

OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy

Haowen Wang, Tao Sun, Kaixiang Ji +3

We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward tr…

cs.IR20242 cited

GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction

Haowen Wang, Yuliang Du, Congyun Jin +5

Predicting click-through rate (CTR) is the core task of many ads online recommendation systems, which helps improve user experience and increase platform revenue. In this type of r…

cs.CL20244 cited

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Qiang Li, Xiaoyan Yang, Haowen Wang +14

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…

cs.LG2023

Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

Haowen Wang, Xinyan Ye, Yangze Zhou +3

Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we c…