activity
20242026
most citedCDEMapper: Enhancing NIH Common Data Element Normalization using Large Language Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CL2026

Ebisu: Benchmarking Large Language Models in Japanese Finance

Xueqing Peng, Ruoyu Xiang, Fan Zhang +9

Japanese finance combines agglutinative, head-final linguistic structure, mixed writing systems, and high-context communication norms that rely on indirect expression and implicit…

cs.CL2026

EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records

Lingfei Qian, Mauro Giuffre, Yan Wang +11

Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language q…

cs.CL2025

When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents

Lingfei Qian, Xueqing Peng, Yan Wang +14

Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies t…

cs.CL2025

RKEFino1: A Regulation Knowledge-Enhanced Large Language Model

Yan Wang, Yueru He, Ruoyu Xiang +1

Recent advances in large language models (LLMs) hold great promise for financial applications but introduce critical accuracy and compliance challenges in Digital Regulatory Report…

cs.CL2025

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application

Xueqing Peng, Lingfei Qian, Yan Wang +44

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…

cs.IR2025

OrdRankBen: A Novel Ranking Benchmark for Ordinal Relevance in NLP

Yan Wang, Lingfei Qian, Xueqing Peng +2

The evaluation of ranking tasks remains a significant challenge in natural language processing (NLP), particularly due to the lack of direct labels for results in real-world scenar…