A Survey on LLM-Based Multi-Agent Systems for Document Mining

PhD Qualifying Examination


Title: "A Survey on LLM-Based Multi-Agent Systems for Document Mining"

by

Mr. Pengze CHEN


Abstract:

Document mining is the process of extracting valuable information and 
knowledge from large collections of documents, such as academic literature, 
analysis reports, and medical records. Efficient document mining is crucial 
for expediting the collection, processing, and utilization of massive 
documents in this era of unprecedented data growth. However, traditional 
methods have often been limited by a shallow understanding of semantics, 
being largely confined to keyword matching and statistical patterns. The true 
potential of document mining is only now being unlocked with the advent of 
Large Language Models (LLMs). Their capabilities in semantic understanding, 
reasoning, and multimodal processing empower them to comprehend and interact 
with documents in a human-like manner. Furthermore, the emergence of 
LLM-based multi-agent systems (LMASs) enhances this process through 
collaborative execution and mitigation of hallucinations, significantly 
boosting the reliability and effectiveness. To systematically investigate the 
advancements in this field, this survey organizes the landscape around three 
representative capabilities: document retrieval, for precisely locating 
relevant documents; document answering, for delivering direct answers; and 
document summarization, for creating condensed and coherent summaries of 
documents. It first analyzes the requirements and challenges of each task, 
and subsequently examines the existing solutions, highlighting their 
respective strengths and weaknesses. Finally, it summarizes the existing 
works and outlines promising research directions to further advance LMAS for 
document mining.


Date:                   Monday, 29 September 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Prof. Lei Chen (Supervisor)
                        Prof. Ke Yi (Chairperson)
                        Dr. Xiaomin Ouyang