開發與評估大型語言模型驅動之臺灣電影產業問答系統：應對專業領域知識散佈之挑戰;Developing and Evaluating a Large Language Model-Powered QA System for Taiwan′s Film Industry: Addressing the Challenge of Dispersed Knowledge

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/96306

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/96306

Title:	開發與評估大型語言模型驅動之臺灣電影產業問答系統：應對專業領域知識散佈之挑戰;Developing and Evaluating a Large Language Model-Powered QA System for Taiwan′s Film Industry: Addressing the Challenge of Dispersed Knowledge
Authors:	郭恩淳;Kuo, En-Chun
Contributors:	資訊管理學系
Keywords:	生成式AI;自然語言處理;檢索增強生成;LangChain開源框架;臺灣電影產業;專業領域問答系統;Generative AI;Retrieval-Augmented Generation (RAG);Large Language Model (LLM);LangChain;Question Answering System;Taiwan Movie Industry
Date:	2024-11-27
Issue Date:	2025-04-09 17:39:08 (UTC+8)
Publisher:	國立中央大學
Abstract:	世界上各個專業領域一直存在著知識散落各處的問題，導致人們在搜尋資料過程耗時，抑或容易找到不精確資料；為了解決專業領域知識不集中等前述問題，此研究以臺灣電影產業為例深入探究，為加速在電影領域工作者或一般民眾查找臺灣電影領域資訊的速度及提高方便性，本研究開發一臺灣電影產業問答系統，運用現今已發展至成熟程度的自然語言處理（NLP）技術及檢索增強生成（RAG）技術，並透過 LangChain 開源框架實現此系統，目的是為幫助相關人士有效地提取相關資訊，同時降低使用現今大眾常使用的生成式AI問答機器人（如：ChatGPT）可能導致的資料洩漏風險。本研究進行了四項實驗，評估此系統與通用型的生成式AI問答機器及商業付費型 RAG 工具的性能表現，結果顯示，本系統在臺灣電影產業領域問題的回答準確度上具有明顯優勢，達到60%以上的準確率。此項研究的研究重要性包括：（1）為台灣首個針對電影產業並使用繁體中文的專屬問答系統雛形，（2）使電影產業專業人士能夠透過簡單的查詢快速獲取可靠答案，從而提升決策效率，並降低資料洩漏風險，（3）讓想涉略各專業領域的人們可以參考此方式，藉以降低搜索資料的困難。本研究同時也討論專業領域問答系統的重要性及結合不斷推陳出新的生成式AI技術，希冀未來能夠應用到其他專業領域或是結合更多型態的資料來源，藉以豐富並強化此臺灣電影領域問答系統。;To address the challenges posed by dispersed domain knowledge in the Taiwanese movie industry, this research introduces a specialized Question Answering (QA) system. The system integrates advancements in Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) technology through open-source platforms LangChain. It is designed to help industry professionals efficiently extract relevant information while minimizing the risk of data leakage associated with general-purpose chatbots. This study conducted four experiments to evaluate the system′s performance against widely used AI chatbots and a commercial RAG tool. The results showed our model′s superior accuracy, achieving over 60% in domain-specific queries, surpassing both generative AI chatbots and the commercial RAG tool. Research significance include: (1) developing Taiwan′s first specialized question-answering system for the film industry using Traditional Chinese, (2) enabling film professionals to quickly access reliable information, enhancing decision-making and reducing data leakage risks, and (3) offering a reference for individuals in various fields to ease information searches. The study also highlights the value of domain-specific QA systems and their integration with evolving generative AI technologies. Future applications may extend to other fields or include more diverse data sources, further strengthening this system for the Taiwanese film industry.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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