大型語言模型與知識圖譜在財經文件上之應用

Abstract

自然語言處理（NLP）和金融數據分析的交匯處代表了一個新興的研究領域，這主要由非結構化金融數據的指數級增長以及對精確解讀這些資訊的複雜工具需求所驅動。本論文探討了大型語言模型（LLMs）和知識圖譜的整合，作為增強金融文件分析的新方法。這項研究的動機源於金融分析不斷演變的格局，越來越依賴於對文本數據的細微解釋來協助決策過程。

論文強調了 LLMs 的作用，它們在這一典範轉移中發揮了重要作用，實現了更準確的情感分析、欺詐檢測和自動化財務報告。文獻綜述追溯了 NLP 應用在金融領域的發展，突顯 LLMs 在從金融文本中提取有意義洞察的關鍵作用。文獻回顧也介紹了知識圖譜的概念，作為一種構建和增強金融數據可解釋性的方法，為後續的實證研究奠定了基礎。

本論文的實證部分提供了一個案例研究，展示在金融數據分析中結合 LLMs 和知識圖譜的實際應用。這部分說明了如何將理論概念應用於解決現實世界的挑戰，重點關注將非結構化數據轉換為結構化格式（即知識圖譜），從而促進更深入的分析和解釋。討論部分深入探討了這種轉換的影響，特別是資訊損失的問題以及減輕這種損失的策略。本部分探討了如何在金融語境中保持原始數據的語義完整性對於準確分析和決策制定的重要性。

論文通過綜合文獻綜述、方法論和案例研究得出的見解作為結論，反思了整合 LLMs 和知識圖譜以革新金融數據分析的潛力，提供了對金融市場更細緻和全面的理解。這項研究為金融領域高級 NLP 技術應用的持續討論做出了貢獻，提出未來可進行研究的方向，並強調在開發和部署這些技術時考慮倫理因素的重要性。

The intersection of Natural Language Processing (NLP) and financial data analysis represents a burgeoning field of study, driven by the exponential growth of unstructured financial data and the need for sophisticated tools to interpret this information accurately. This thesis explores the integration of Large Language Models (LLMs) and knowledge graphs as a novel approach to enhance the analysis of financial documents. The motivation behind this research stems from the evolving landscape of financial analysis, which increasingly relies on the nuanced interpretation of textual data to inform decision-making processes.

The thesis emphasizes the role of LLMs which have been instrumental in this paradigm shift, enabling more accurate sentiment analysis, fraud detection, and automated financial reporting. A review of the literature traces the development of NLP applications within the financial domain, highlighting the critical role of LLMs in extracting meaningful insights from financial texts. This section also introduces the concept of knowledge graphs as a means to structure and enhance the interpretability of financial data, providing a foundation for the subsequent empirical investigation. The empirical component of the thesis presents a case study that exemplifies the practical application of combining LLMs with knowledge graphs in financial data analysis. This section illustrates how theoretical concepts can be applied to address real-world challenges, focusing on the transformation of unstructured data into structured formats, i.e. knowledge graphs, that facilitate deeper analysis and interpretation. The discussion section delves into the implications of this transformation, particularly the issue of information loss and strategies to mitigate it. It explores how maintaining the semantic integrity of the original data is crucial for accurate analysis and decision-making in financial contexts.

The thesis concludes by synthesizing the insights gained from the literature review, methodology, and case study. It reflects on the potential of integrating LLMs with knowledge graphs to revolutionize financial data analysis, offering a more nuanced and comprehensive understanding of financial markets. This research contributes to the ongoing discourse on the application of advanced NLP techniques in finance, suggesting directions for future inquiry and highlighting the importance of ethical considerations in the development and deployment of these technologies.