4 citations · 4 across the 5 of their papers we have counts for
6 papers
Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory
Qian Chen, Xianyin Zhang, Lifan Guo +1
Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page-level elements. However, do…
Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset
Qian Chen, Xianyin Zhang, Yanzhi Liu +3
The emergence of Large Vision-Language Models (LVLMs) has substantially expanded model capabilities beyond text-only understanding, enabling unified inference across both visual an…
FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol
Jie Zhu, Yimin Tian, Boyang Li +8
This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of finan…
DianJin-OCR-R1: Enhancing OCR Capabilities via a Reasoning-and-Tool Interleaved Vision-Language Model
Qian Chen, Xianyin Zhang, Lifan Guo +2
Recent advances in vision-language models (VLMs) have enabled end-to-end document parsing and understanding, achieving strong performance on diverse optical character recognition (…
FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
Yingqian Wu, Qiushi Wang, Zefei Long +7
Financial report generation tasks range from macro- to micro-economics analysis, also requiring extensive data analysis. Existing LLM models are usually fine-tuned on simple QA tas…
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
Wei Chen, Qiushi Wang, Zefei Long +8
We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM. Our methodology improves general LLMs by endowing them with multi-tu…