政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/147747

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/147747

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题名:	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
作者:	宋浩茹 Sung, Hao-Ru
贡献者:	李蔡彥黃瀚萱 Li, Tsai-Yen Huang, Hen-Hsen 宋浩茹 Sung, Hao-Ru
关键词:	自然語言生成時間知識建模摘要更新自監督學習 Natural Language Generation Temporal Knowledge Modeling Update Summarization Self-Supervision
日期:	2023
上传时间:	2023-10-03 10:49:40 (UTC+8)
摘要:	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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描述:	碩士國立政治大學資訊科學系 110753124
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110753124
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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