Assembling Fragmented Domain Knowledge: A LLM-Powered QA System for Taiwan Cinema
En-Chun Kuo, Yea-Huey Su · 2024
This research introduces the development of a specialized Question Answering (QA) system, designed to tackle the challenges posed by dispersed domain knowledge. Specifically tailored for the Taiwanese movie industry, this system utilizes advancements in Natural Language Processing (NLP) and incorporates large language models via open-source platforms like LangChain. Our aim is to facilitate industry professionals in swiftly locating and extracting pertinent information from extensive data resources. A key focus is on mitigating the risk of data leakage, which is often associated with uploading documents to general-purpose chatbots. We have conducted a comprehensive evaluation of our Large Language Model (LLM)-powered QA system, showcasing its efficacy and accuracy in response. Ultimately, this research strives to illuminate the complexities of aggregating scattered expertise, aiding those who seek to delve deeply into domain-specific knowledge.