政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153391

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153391

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/153391

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題名:	基於大型語言模型之提示詞策略於改進文件檢索效能 Prompting Large Language Models for Improving Document Retrieval Performance
作者:	蕭喬宇 Hsiao, Chiao-Yu
貢獻者:	蔡銘峰 Tsai, Ming-Feng 蕭喬宇 Hsiao, Chiao-Yu
關鍵詞:	資訊檢索大型語言模型重排序列表排序 Information Retrieval LLM Reordering List-wise Reordering
日期:	2024
上傳時間:	2024-09-04 15:02:11 (UTC+8)
摘要:	本研究旨在探討如何利用大型語言模型（LLM）進行列表重排序，以提升文件檢索的性能。文件檢索通常分為兩個階段：第一階段是檢索器，用於從大型文檔庫中檢索相關文檔；第二階段是生成器，根據檢索到的文檔生成適當的回答。重排序技術通過對初步檢索到的文檔進行進一步的精細排序，確保生成器接收到最相關和最有價值的文檔，從而提升回答的準確性和相關性。近年來，LLM在重排序任務中的應用已成為趨勢，LLM以其強大的語言理解和生成能力，能夠更好地捕捉文檔間的語義相關性，並更準確地識別與查詢相關的文檔。儘管 LLM 具有提升重排序性能的潛力，但在實際應用中仍存在一系列限制，例如模型在某些情況下可能存在幻覺和不確定性，這可能導致重排序結果不合理或不準確；為了解決這些問題，本研究使用了以大型語言模型為核心的列表重排序。具體而言，我們使用查詢和相關段落列表作為提示，讓模型回答最相關的文檔排序。為了進一步提高重排序的準確性和性能，我們探討了多種提示策略，包括改寫前綴提示詞、引入索引標記和上下文學習等。此外，針對 LLM 的提示長度限制，本研究提出了針對文檔的前處理方法，包括段落文字處理及轉換、關鍵字提取及段落摘要生成等，以減少文檔長度並最大程度保留文檔的資訊量。通過系統性的實驗驗證，我們得出了這些策略對於提升文件檢索性能的有效性，特別是對於基礎性能較差的檢索器，提升幅度可達一倍以上；綜合上述，這項研究對於改進文件檢索中文檔重排序的品質提供了有價值的方法和啟示，不僅能夠提升系統的性能，還有助於推動檢索技術的發展，使其在實際應用中更加有效和可靠。 This study aims to explore how to use large language models (LLM) for listwise reordering to improve document retrieval performance. Document retrieval typically consists of two stages: the first stage involves a retriever that fetches relevant documents from a large corpus, and the second stage involves a generator that produces appropriate responses based on the retrieved documents. Reordering techniques refine the initially retrieved documents to ensure the generator receives the most relevant and valuable documents, thus enhancing the accuracy and relevance of the generated responses. Recently, the application of LLMs in reordering tasks has become a noticeable trend. With their powerful language understanding and generation capabilities, LLMs can better capture the semantic relevance between documents and more accurately identify documents related to the query. Despite the potential of LLMs to enhance reordering performance, practical applications still face several limitations, such as hallucinations and uncertainties that may lead to unreasonable or inaccurate reordering results. To address these issues, this study proposes a listwise reordering method centered on LLMs. Specifically, we use queries and lists of relevant passages as prompts to guide the model in determining the most relevant document order. To further improve reordering accuracy and performance, we explored various prompt strategies, including prefix rewriting, index tagging, and in-context learning. Additionally, to address the prompt length limitations of LLMs, we developed preprocessing methods for documents, including text processing, keyword extraction, and paragraph summarization, to reduce document length while preserving as much information as possible. Through systematic experiments, we verified the effectiveness of these strategies in enhancing document retrieval performance, especially for retrievers with lower baseline performance, where improvements can be more than doubled. In summary, this study provides valuable methods and insights for improving the quality of document reordering in document retrieval, enhancing system performance, and advancing the development of retrieval techniques, making them more effective and reliable in practical applications.
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描述:	碩士國立政治大學資訊科學系 111753203
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111753203
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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