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題名 透過眼動選擇大型語言模型建議達成符合使用者意圖之人工智慧協作擴增實境筆記系統
Co-Piloted AR Note-Taking via Gaze Selection of LLM Suggestions to Match Users’ Intentions
作者 裘世綱
Chiu, Shih Kang
貢獻者 蔡欣叡
Tsai, Hsin-Ruey
裘世綱
Chiu, Shih Kang
關鍵詞 擴增實境
人工智慧
大型語言模型
眼動追蹤
Augmented Reality
Artificial Intelligence
Large Language Model
Eye Tracking
日期 2024
上傳時間 4-九月-2024 15:00:19 (UTC+8)
摘要 記錄筆記在聽演講和與人討論時至關重要，不僅用於幫助我們紀錄摘要和總結重點，還會在提問環節中用於提醒想提出的問題或提醒在討論時的發言。然而，以手機打字的筆記方式可能會分散使用者注意力並增加使用者的心智負擔。儘管大型語言模型（LLM）能夠被用於自動生成演講或討論的摘要和重點，但如果直接由人工智慧（AI）生成，沒有使用者參與或互動，得到的結果未必能符合使用者的意圖。因此，我們提出GazeNoter，一個人工智慧協作擴增實境（AR）系統，讓使用者透過AR頭戴裝置的眼動追蹤，快速選擇LLM生成的建議，以達成實時筆記。GazeNoter利用AR頭戴裝置作為媒介，讓使用者能夠快速給予LLM回饋以產出更符合其意圖之筆記，形成一個使用者參與式的AI系統，且不僅可產出演講與討論的內容以內的筆記，甚至能將筆記延伸至內容之外。我們進行了兩項使用者研究，分別驗證GazeNoter在聽演講的靜態情境和行走會議的移動情境下進行即時筆記的可用性。
Note-taking is critical during speeches and discussions, serving not only for later summarization and organization but also for real-time question and opinion reminding in question-and-answer sessions or timely contributions in discussions. Manually typing on smartphones for note-taking could be distracting and increase cognitive load for users. While large language models (LLMs) are used to automatically generate summaries and highlights, the con- tent generated by artificial intelligence (AI) may not match users’intentions without user input or interaction. Therefore, we propose an AI-copiloted augmented reality (AR) system, GazeNoter, to allow users to swiftly select diverse LLM-generated suggestions via gaze on an AR headset for real-time note-taking. GazeNoter leverages an AR headset as a medium for users to swiftly adjust the LLM output to match their intentions, forming a user-in-the-loop AI system for both within-context and beyond-context notes. We conducted two user studies to verify the usability of GazeNoter in attending speeches in a static sitting condition and walking meetings and discussions in a mobile walking condition, respectively.
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描述 碩士
國立政治大學
資訊科學系
111753142
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111753142
資料類型 thesis
dc.contributor.advisor 蔡欣叡zh_TW
dc.contributor.advisor Tsai, Hsin-Rueyen_US
dc.contributor.author (作者) 裘世綱zh_TW
dc.contributor.author (作者) Chiu, Shih Kangen_US
dc.creator (作者) 裘世綱zh_TW
dc.creator (作者) Chiu, Shih Kangen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-九月-2024 15:00:19 (UTC+8)-
dc.date.available 4-九月-2024 15:00:19 (UTC+8)-
dc.date.issued (上傳時間) 4-九月-2024 15:00:19 (UTC+8)-
dc.identifier (其他 識別碼) G0111753142en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153381-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 111753142zh_TW
dc.description.abstract (摘要) 記錄筆記在聽演講和與人討論時至關重要，不僅用於幫助我們紀錄摘要和總結重點，還會在提問環節中用於提醒想提出的問題或提醒在討論時的發言。然而，以手機打字的筆記方式可能會分散使用者注意力並增加使用者的心智負擔。儘管大型語言模型（LLM）能夠被用於自動生成演講或討論的摘要和重點，但如果直接由人工智慧（AI）生成，沒有使用者參與或互動，得到的結果未必能符合使用者的意圖。因此，我們提出GazeNoter，一個人工智慧協作擴增實境（AR）系統，讓使用者透過AR頭戴裝置的眼動追蹤，快速選擇LLM生成的建議，以達成實時筆記。GazeNoter利用AR頭戴裝置作為媒介，讓使用者能夠快速給予LLM回饋以產出更符合其意圖之筆記，形成一個使用者參與式的AI系統，且不僅可產出演講與討論的內容以內的筆記，甚至能將筆記延伸至內容之外。我們進行了兩項使用者研究，分別驗證GazeNoter在聽演講的靜態情境和行走會議的移動情境下進行即時筆記的可用性。zh_TW
dc.description.abstract (摘要) Note-taking is critical during speeches and discussions, serving not only for later summarization and organization but also for real-time question and opinion reminding in question-and-answer sessions or timely contributions in discussions. Manually typing on smartphones for note-taking could be distracting and increase cognitive load for users. While large language models (LLMs) are used to automatically generate summaries and highlights, the con- tent generated by artificial intelligence (AI) may not match users’intentions without user input or interaction. Therefore, we propose an AI-copiloted augmented reality (AR) system, GazeNoter, to allow users to swiftly select diverse LLM-generated suggestions via gaze on an AR headset for real-time note-taking. GazeNoter leverages an AR headset as a medium for users to swiftly adjust the LLM output to match their intentions, forming a user-in-the-loop AI system for both within-context and beyond-context notes. We conducted two user studies to verify the usability of GazeNoter in attending speeches in a static sitting condition and walking meetings and discussions in a mobile walking condition, respectively.en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1 CHAPTER 2 RELATED WORK 5 2.1 AUGMENTING AR WITH NATURAL LANGUAGE 5 2.2 NOTE-TAKING APPROACHES 6 2.3 USER-IN-THE-LOOP NLP SYSTEMS 7 2.4 GAZE SELECTION AND LAYOUT ON HEADSETS 9 CHAPTER 3 GAZENOTER 11 3.1 DESIGN CONSIDERATIONS 12 3.2 GAZENOTER FEATURES AND FLOW 16 3.3 AR GAZE SELECTION 20 3.4 NOTE-TAKING SYSTEM IMPLEMENTATION 23 CHAPTER 4 USER STUDY 1: FORMAL SPEECH 26 4.1 PARTICIPANTS AND APPARATUS 26 4.2 TASK AND PROCEDURE 27 4.3 RESULTS AND DISCUSSION 30 CHAPTER 5 USER STUDY 2: WALKING MEETING 38 5.1 SETUP, TASK AND PROCEDURE 39 5.2 RESULTS AND DISCUSSION 40 CHAPTER 6 LIMITATIONS AND FUTURE WORK 47 CHAPTER 7 CONCLUSION 48 REFERENCES 49 APPENDIX 67zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111753142en_US
dc.subject (關鍵詞) 擴增實境zh_TW
dc.subject (關鍵詞) 人工智慧zh_TW
dc.subject (關鍵詞) 大型語言模型zh_TW
dc.subject (關鍵詞) 眼動追蹤zh_TW
dc.subject (關鍵詞) Augmented Realityen_US
dc.subject (關鍵詞) Artificial Intelligenceen_US
dc.subject (關鍵詞) Large Language Modelen_US
dc.subject (關鍵詞) Eye Trackingen_US
dc.title (題名) 透過眼動選擇大型語言模型建議達成符合使用者意圖之人工智慧協作擴增實境筆記系統zh_TW
dc.title (題名) Co-Piloted AR Note-Taking via Gaze Selection of LLM Suggestions to Match Users’ Intentionsen_US
dc.type (資料類型) thesisen_US
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