16 citations · 20 across the 5 of their papers we have counts for
6 papers
UP-MOPD: Update Projection in Multi-Teacher On-Policy Distillation
Taojie Zhu, Jing Jin, Yuan Xia +8
On-policy distillation from multiple teachers combines expertise from different domains in a single student, but conflicting gradients can hinder this integration. Gradient correct…
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering
Tianchi Cai, Zhiwen Tan, Xierui Song +5
Retrieval Augmented Generation (RAG) has become prevalent in question-answering (QA) tasks due to its ability of utilizing search engine to enhance the quality of long-form questio…
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.…
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…
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…
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Haowen Wang, Tao Sun, Cong Fan +1
Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects…