6 papers
An Entity Linking Agent for Question Answering
Yajie Luo, Yihong Wu, Muzhi Li +5
Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. Entity Linking (EL) plays a critical role in linking natural language mentions to KB…
: A Route-to-Rerank Post-Training Framework for Multi-Domain Decoder-Only Rerankers
Xinyu Wang, Hanwei Wu, Qingchen Hu +13
Decoder-only rerankers are central to Retrieval-Augmented Generation (RAG). However, generalist models miss domain-specific nuances in high-stakes fields like finance and law, and…
VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu +24
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from…
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Suyuchen Wang, Jinlin Wang, Xinyu Wang +6
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches ei…
STRICT: Stress Test of Rendering Images Containing Text
Tianyu Zhang, Xinyu Wang, Lu Li +5
While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle to generate consistent…
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering
Xinyu Wang, Jijun Chi, Zhenghan Tai +13
Leveraging large language models in real-world settings often entails a need to utilize domain-specific data and tools in order to follow the complex regulations that need to be fo…