153 citations · 215 across the 11 of their papers we have counts for
12 papers
FinNextAssist: Towards Professional Financial Deep Research Assistant
Xiangyu Li, Fengbin Zhu, Xuan Yao +9
Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and stru…
FinDeepForecast: A Live Multi-Agent System for Benchmarking Deep Research Agents in Financial Forecasting
Xiangyu Li, Xuan Yao, Guohao Qi +16
Deep Research (DR) Agents powered by advanced Large Language Models (LLMs) have fundamentally shifted the paradigm for completing complex research tasks. Yet, a comprehensive and l…
FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis
Fengbin Zhu, Xiang Yao Ng, Ziyang Liu +19
Deep Research (DR) agents, powered by advanced Large Language Models (LLMs), have recently garnered increasing attention for their capability in conducting complex research tasks.…
Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance
Fengbin Zhu, Junfeng Li, Liangming Pan +5
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce…
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Fengbin Zhu, Ziyang Liu, Xiang Yao Ng +6
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insuffic…
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
Fengbin Zhu, Ziyang Liu, Fuli Feng +3
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e.g. SEC filings), where discrete reasoning capa…
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Fengbin Zhu, Chao Wang, Fuli Feng +3
Discrete reasoning over table-text documents (e.g., financial reports) gains increasing attention in recent two years. Existing works mostly simplify this challenge by manually sel…
Towards Complex Document Understanding By Discrete Reasoning
Fengbin Zhu, Wenqiang Lei, Fuli Feng +3
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural La…
RDU: A Region-based Approach to Form-style Document Understanding
Fengbin Zhu, Chao Wang, Wenqiang Lei +2
Key Information Extraction (KIE) is aimed at extracting structured information (e.g. key-value pairs) from form-style documents (e.g. invoices), which makes an important step towar…
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang +5
Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is la…
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering
Fengbin Zhu, Wenqiang Lei, Chao Wang +3
Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-sc…
Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang, Yang Liu, Chao Wang +2
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora…