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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳炳宇 | zh_TW |
| dc.contributor.advisor | Bing-Yu Chen | en |
| dc.contributor.author | 李旻叡 | zh_TW |
| dc.contributor.author | Min-Jui Lee | en |
| dc.date.accessioned | 2024-09-15T16:45:16Z | - |
| dc.date.available | 2024-09-16 | - |
| dc.date.copyright | 2024-09-14 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-11 | - |
| dc.identifier.citation | T. L. Adams. Reading mathematics: More than words can say. The reading teacher, 56(8):786–795, 2003.
L. Alcock. e-proofs: Student experience of online resources to aid understanding of mathematical proofs. In Proceedings of the 12th Conference on Research in Undergraduate Mathematics Education. Raleigh, NC: Special Interest Group of the Mathematical Association of America on Research in Undergraduate Mathematics Education. Citeseer, 2009. M. Alexeeva, R. Sharp, M. A. Valenzuela-Esc´arcega, J. Kadowaki, A. Pyarelal, and C. Morrison. Mathalign: Linking formula identifiers to their contextual natural language descriptions. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2204–2212, 2020. S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. N. Bennett, K. Inkpen, et al. Guidelines for human-ai interaction. In Proceedings of the 2019 chi conference on human factors in computing systems, pages 1–13, 2019. K. Azad. Colorized math equations. Better Explained, 2017. A. Bangor, P. T. Kortum, and J. T. Miller. An empirical evaluation of the system usability scale. Intl. Journal of Human–Computer Interaction, 24(6):574–594, 2008. J. Brooke. Sus: a “quick and dirty’usability. Usability evaluation in industry, 189(3):189–194, 1996. N. Chinchor and B. M. Sundheim. Muc-5 evaluation metrics. In Fifth Message Understanding Conference (MUC-5): Proceedings of a Conference Held in Baltimore, Maryland, August 25-27, 1993, 1993. N. Chulpongsatorn, M. S. Lunding, N. Soni, and R. Suzuki. Augmented math: Authoring ar-based explorable explanations by augmenting static math textbooks. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pages 1–16, 2023. M. Conlen and J. Heer. Idyll: A markup language for authoring and publishing interactive articles on the web. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pages 977–989, 2018. V. F. de Santana, R. de Oliveira, L. D. A. Almeida, and M. C. C. Baranauskas. Web accessibility and people with dyslexia: a survey on techniques and guidelines. In Proceedings of the international cross-disciplinary conference on web accessibility, pages 1–9, 2012. Y. Deng, A. Kanervisto, J. Ling, and A. M. Rush. Image-to-markup generation with coarseto-fine attention. In International Conference on Machine Learning, pages 980–989. PMLR, 2017. A. N. Dragunov and J. L. Herlocker. Designing intelligent and dynamic interfaces for communicating mathematics. In Proceedings of the 8th international conference on Intelligent user interfaces, pages 236–238, 2003. M. D’Zmura. Color in visual search. Vision research, 31(6):951–966, 1991. D. Ferreira, M. Thayaparan, M. Valentino, J. Rozanova, and A. Freitas. To be or not to be an integer? encoding variables for mathematical text. In Findings of the Association for Computational Linguistics: ACL 2022, pages 938–948, 2022. R. Fok, H. Kambhamettu, L. Soldaini, J. Bragg, K. Lo, M. Hearst, A. Head, and D. S. Weld. Scim: Intelligent skimming support for scientific papers. In Proceedings of the 28th International Conference on Intelligent User Interfaces, pages 476–490, 2023. K. I. Gero, V. Liu, and L. Chilton. Sparks: Inspiration for science writing using language models. In Proceedings of the 2022 ACM Designing Interactive Systems Conference, pages 1002–1019, 2022. L. Giray. Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633, 2023. C. Gobert and M. Beaudouin-Lafon. i-latex: Manipulating transitional representations between latex code and generated documents. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–16, 2022. S. G. Hart. Nasa-task load index (nasa-tlx); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting, volume 50, pages 904–908. Sage publications Sage CA: Los Angeles, CA, 2006. A. Head, K. Lo, D. Kang, R. Fok, S. Skjonsberg, D. S.Weld, and M. A. Hearst. Augmenting scientific papers with just-in-time, position-sensitive definitions of terms and symbols. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–18, 2021. A. Head, A. Xie, and M. A. Hearst. Math augmentation: How authors enhance the readability of formulas using novel visual design practices. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–18, 2022. C. G. Healey. Choosing effective colours for data visualization. In Proceedings of Seventh Annual IEEE Visualization’96, pages 263–270. IEEE, 1996. J. Heer, M. Conlen, V. Devireddy, T. Nguyen, and J. Horowitz. Living papers: A language toolkit for augmented scholarly communication. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pages 1–13, 2023. F. Hohman, M. Conlen, J. Heer, and D. H. P. Chau. Communicating with interactive articles. Distill, 5(9):e28, 2020. F. Hohman and other contributors. Awesome mathematical notation design. 2020. H. Jo, D. Kang, A. Head, and M. A. Hearst. Modeling mathematical notation semantics in academic papers. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3102–3115, 2021. H. B. Kang, T.Wu, J. C. Chang, and A. Kittur. Synergi: A mixed-initiative system for scholarly synthesis and sensemaking. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23, New York, NY, USA, 2023. Association for Computing Machinery. J. Kim, S. Suh, L. B. Chilton, and H. Xia. Metaphorian: Leveraging large language models to support extended metaphor creation for science writing. In Proceedings of the 2023 ACM Designing Interactive Systems Conference, pages 115–135, 2023. A. Kohlhase, M. Kohlhase, and T. Ouypornkochagorn. Discourse phenomena in mathematical documents. In Intelligent Computer Mathematics: 11th International Conference, CICM 2018, Hagenberg, Austria, August 13-17, 2018, Proceedings 11, pages 147–163. Springer, 2018. V. D. Lai, A. P. B. Veyseh, F. Dernoncourt, and T. H. Nguyen. Semeval 2022 task 12: Symlink-linking mathematical symbols to their descriptions. arXiv preprint arXiv:2202.09695, 2022. Y. Lai, A. Kankanhalli, and D. Ong. Human-ai collaboration in healthcare: A review and research agenda. 2021. S.-M. Lee and S.-H. Na. Jbnu-cclab at semeval-2022 task 12: Machine reading comprehension and span pair classification for linking mathematical symbols to their descriptions. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1679–1686, 2022. P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35, 2023. J. Meadows and A. Freitas. Introduction to mathematical language processing: Informal proofs, word problems, and supporting tasks. Transactions of the Association for Computational Linguistics, 11:1162–1184, 2023. A. Miniukovich, A. De Angeli, S. Sulpizio, and P. Venuti. Design guidelines for web readability. In Proceedings of the 2017 Conference on Designing Interactive Systems, pages 285–296, 2017. M. O¨ sterholm. Characterizing reading comprehension of mathematical texts. Educational studies in mathematics, 63:325–346, 2006. R. Pagael and M. Schubotz. Mathematical language processing project. arXiv preprint arXiv:1407.0167, 2014. M. Schubotz, A. Grigorev, M. Leich, H. S. Cohl, N. Meuschke, B. Gipp, A. S. Youssef, and V. Markl. Semantification of identifiers in mathematics for better math information retrieval. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 135–144, 2016. O. Shaikh, J. Saad-Falcon, A. P. Wright, N. Das, S. Freitas, O. Asensio, and D. H. Chau. Energyvis: interactively tracking and exploring energy consumption for ml models. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–7, 2021. D. Wang, E. Churchill, P. Maes, X. Fan, B. Shneiderman, Y. Shi, and Q. Wang. From human-human collaboration to human-ai collaboration: Designing ai systems that can work together with people. In Extended abstracts of the 2020 CHI conference on human factors in computing systems, pages 1–6, 2020. D.Wang, J. D.Weisz, M. Muller, P. Ram, W. Geyer, C. Dugan, Y. Tausczik, H. Samulowitz, and A. Gray. Human-ai collaboration in data science: Exploring data scientists’ perceptions of automated ai. Proceedings of the ACM on human-computer interaction, 3(CSCW):1–24, 2019. F. Wang, X. Liu, O. Liu, A. Neshati, T. Ma, M. Zhu, and J. Zhao. Slide4n: Creating presentation slides from computational notebooks with human-ai collaboration. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–18, 2023. J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022. M. Wolska and M. Grigore. Symbol declarations in mathematical writing. 2010. Z. Wu, J. Li, K. Ma, H. Kambhamettu, and A. Head. Ffl: A language and live runtime for styling and labeling typeset math formulas. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pages 1–16, 2023. G. Wyszecki and W. S. Stiles. Color science: concepts and methods, quantitative data and formulae, volume 40. John wiley & sons, 2000. K. G. Yoko, M.-Q. Nghiem, Y. Matsubayashi, and A. Aizawa. Extracting definitions of mathematical expressions in scientific papers. In Proceedings of the 26th Annual Conference of JSAI, pages 1–7, 2012. H. I. Yung and F. Paas. Effects of computer-based visual representation on mathematics learning and cognitive load. 2015. J. Zamfirescu-Pereira, R. Y. Wong, B. Hartmann, and Q. Yang. Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–21, 2023. W. X. Zhao, K. Zhou, J. Li, T. Tang, X.Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023. Q. Zheng, Y. Tang, Y. Liu, W. Liu, and Y. Huang. Ux research on conversational human-ai interaction: A literature review of the acm digital library. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–24, 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95677 | - |
| dc.description.abstract | 讀懂數學式是閱讀科學文章時的必要任務之一。閱讀數學式時，讀者需要找出其中各個符號的定義，而良好的視覺化與結構化的文字
解釋，能夠增強這些數學式可讀性。然而，這些設計需要仰賴文章作者的編輯，過程不僅繁瑣且費時。為此，我們製作了DefExtractor，一個基於大語言模型的編輯工具，能夠幫助文章作者提取並視覺化數學標識符與對應的定義。只要給定由LaTeX編輯的數學式與解釋性文字，DefExtractor便能利用大語言模型辨識其語意，推薦適合的上色設計。對於模型的推薦設計，使用者還能夠進一步提出改正建議讓模型改正，以此達到人與AI模型的雙向互動與協作。我們進行了技術評估，發現DefExtractor的標識符-定義對自動提取流程，在目標使用情境下優於過去的模型。而一項包含十二位受試者的使用者研究，顯示DefExtractor中AI的輔助，能夠有效降低使用者負荷並縮短編輯時間。 | zh_TW |
| dc.description.abstract | Following mathematical formulas is a critical task in scientific paper reading. Readers would trace the definition and the relation of identifiers in a formula. To enhance the readability, it relies on paper authors to create effective visualization and structured text explanations, which is especially challenging for long formulas. We propose DefExtractor, an LLM-based tool that assists authors in extracting and visualizing identifier-definition pairs with AI interactively. Given a LATEX input, DefExtractor identifies the semantics and automatically suggests colored identifiers and definitions based on the LLM response. Users can modify via text prompt or syntax, where AI adapts the edits iteratively. A technical evaluation showed our pair extraction pipeline outperforms previous model in our target scenario, and a usability study with 12 participants showed that DefExtractor effectively reduced the workloads of authors and shortened editing time compared with a baseline tool. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-09-15T16:45:16Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-09-15T16:45:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 ii
中文摘要 iii Abstract iv List of Figures vii List of Tables x Chapter 1 Introduction 1 Chapter 2 Related Work 6 2.1 Reading Augmentation in Math . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Identifier-Definition Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Human-AI Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Chapter 3 Design Process 10 3.1 Early prototype: MaugVLink . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Preliminary User Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Design Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Chapter 4 DefExtractor 19 4.1 Collaboration Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Interactive Interface and Output Creation . . . . . . . . . . . . . . . . . . . 22 4.3 Prompt Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 5 Technical Evaluation 25 5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 6 User Study 29 6.1 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.1.2 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.1.3 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.2 Results and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6.2.1 Task Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6.2.2 Workload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.2.3 Rating and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Chapter 7 Limitations and Future Work 37 7.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 7.2 Compatibility With Other Authoring Tools . . . . . . . . . . . . . . . . . . 37 7.3 Mathematical Formulas Construction . . . . . . . . . . . . . . . . . . . . . 38 Chapter 8 Conclusion 39 Bibliography 40 Chapter 9 Appendix 46 | - |
| dc.language.iso | en | - |
| dc.title | DefExtractor: 基於大語言模型雙向互動的數學標識符-定義對的自動提取與視覺化 | zh_TW |
| dc.title | DefExtractor: LLM-Based Automatic Extraction and Visualization of Mathematical Identifier-Definition Pairs with Bidirectional Interaction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林文杰;張鈞法 | zh_TW |
| dc.contributor.oralexamcommittee | Wen-Chieh Lin;Chun-Fa Chang | en |
| dc.subject.keyword | 數學式,數學符號,編輯工具,視覺化,大語言模型,LaTeX, | zh_TW |
| dc.subject.keyword | Mathematical formulas,Mathematical notation,Authoring tools,Visualization,LLM,LaTeX, | en |
| dc.relation.page | 48 | - |
| dc.identifier.doi | 10.6342/NTU202403869 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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