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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/147747
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147747

    Title: 基於自監督學習之生成語言模型序列文本知識更新
    Sequential Text-based Knowledge Update with Self-Supervised Learning for Generative Language Models
    Authors: 宋浩茹
    Sung, Hao-Ru
    Contributors: 李蔡彥

    Li, Tsai-Yen
    Huang, Hen-Hsen

    Sung, Hao-Ru
    Keywords: 自然語言生成
    Natural Language Generation
    Temporal Knowledge Modeling
    Update Summarization
    Date: 2023
    Issue Date: 2023-10-03 10:49:40 (UTC+8)
    Abstract: 本研究提出新的自然語言處理(NLP)任務,以解決多輪、序列式的文本知識更新問題。該研究引入了一種混合學習架構和新穎的自監督訓練策略,旨在使生成語言模型能夠像人類一樣有效地鞏固和更新知識。這種方式對於改善語言模型的學習和理解能力具有重大意義。為了驗證這種策略的有效性,我們還創建了一個新的數據集以進行評估。從實驗結果來看,我們的方法在效能上超越了現有的模型和GPT-3.5-Turbo。本研究所提出的任務和模型架構能夠提升知識組織的自動化程度,使得基於文本知識的大型語言模型(LLM),成為協助人類執行各種任務的重要資源。
    This work proposes a new natural language processing (NLP) task to tackle the issue of multi-round, sequential text-based knowledge update. The study introduces a hybrid learning architecture and a novel self-supervised training strategy to enable generative language models to consolidate knowledge in the same way as humans. A dataset was also created for evaluation and results showed the effectiveness of our methodology. Experimental results confirm the superiority of the proposed approach over existing models and GPT-3.5-Turbo. The proposed task and model framework have the potential to significantly improve the automation of knowledge organization, making text-based knowledge an increasingly crucial resource for powerful large language models (LLM) to perform various tasks for humans.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753124
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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