Relevant bibliographies by topics / Large Language

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Large Language'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Large Language"

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Kumar, Deepak, Dr Amandeep, Pinki Pinki, et al. "Visualization by Natural Language Processing and Large Language Model." International Journal of Research Publication and Reviews 6, no. 6 (2025): 11225–31. https://doi.org/10.55248/gengpi.6.0625.2340.

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Baral, Elina, and Sagar Shrestha. "Large Vocabulary Continuous Speech Recognition for Nepali Language." International Journal of Signal Processing Systems 8, no. 4 (2020): 68–73. http://dx.doi.org/10.18178/ijsps.8.4.68-73.

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Speech Recognition is a widely studied topic for high-resource languages like English and Mandarin. A plethora of publications exist that study the performance of several recognition methods for these languages. However differences in phonetics, accent, language model, etc between any two different languages demand for a study of speech recognition methodologies and components separately for each language. In this paper, we present a comparative study of popular speech recognition methods for Nepali, a low-resource Indo-Aryan language. We describe our approach to building the phonetic dictiona
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Sharma Shria Verma, Dhananjai. "Automated Penetration Testing using Large Language Models." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 1826–31. http://dx.doi.org/10.21275/sr24427043741.

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Pooley, Jefferson. "Large Language Publishing." KULA: Knowledge Creation, Dissemination, and Preservation Studies 7, no. 1 (2024): 1–11. http://dx.doi.org/10.18357/kula.291.

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The AI hype cycle has come for scholarly publishing. This essay argues that the industry’s feverishーif mostly aspirationalーembrace of artificial intelligence should be read as the latest installment of an ongoing campaign. Led by Elsevier, commercial publishers have, for about a decade, layered a second business on top of their legacy publishing operations. That business is to mine and process scholars’ works and behavior into prediction products, sold back to universities and research agencies. This article focuses on an offshoot of the big firms’ surveillance-publishing businesses: the post-
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Cerf, Vinton G. "Large Language Models." Communications of the ACM 66, no. 8 (2023): 7. http://dx.doi.org/10.1145/3606337.

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Chauhan, Satyam. "Knowledge Discovery in Databases Utilizing Large Language Models." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1886–94. http://dx.doi.org/10.21275/ms241026170018.

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Son, Seok-Bin, Joongheon Kim, Changsik Cho, and Soohyun Park. "Trends in Network Optimization Using Large Language Models." Journal of Korean Institute of Communications and Information Sciences 50, no. 7 (2025): 1073–84. https://doi.org/10.7840/kics.2025.50.7.1073.

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Mishra, Vinaytosh. "Large Language Models in Medical Education and Quality Concerns." Journal of Quality in Health Care & Economics 6, no. 1 (2023): 1–3. http://dx.doi.org/10.23880/jqhe-16000319.

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Jain, Migul. "Future of Interacting with Computers and Large Language Models." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1711–12. http://dx.doi.org/10.21275/sr231023121603.

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Hong, Lichan, Gregorio Convertino, and Ed Chi. "Language Matters In Twitter: A Large Scale Study." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 518–21. http://dx.doi.org/10.1609/icwsm.v5i1.14184.

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Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51%). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39% of the tweets. Examining users of the top 10 languages, w
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Dissertations / Theses on the topic "Large Language"

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McGarry, Theresa, and J. Mwinyelle. "Inductive Language Teaching in Large Classes." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etsu-works/6165.

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Labeau, Matthieu. "Neural language models : Dealing with large vocabularies." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.

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Le travail présenté dans cette thèse explore les méthodes pratiques utilisées pour faciliter l'entraînement et améliorer les performances des modèles de langues munis de très grands vocabulaires. La principale limite à l'utilisation des modèles de langue neuronaux est leur coût computationnel: il dépend de la taille du vocabulaire avec laquelle il grandit linéairement. La façon la plus aisée de réduire le temps de calcul de ces modèles reste de limiter la taille du vocabulaire, ce qui est loin d'être satisfaisant pour de nombreuses tâches. La plupart des méthodes existantes pour l'entraînement
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Jiang, Yuandong. "Large Scale Distributed Semantic N-gram Language Model." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316200173.

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Tang, Haijiang. "Building phrase based language model from large corpus /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20TANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 74-79). Also available in electronic version. Access restricted to campus users.
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Cho, Chung Yik. "Large Scale Data Analytics with Language Integrated Query." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/73570.

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Databases can easily reach petabytes (1,048,576 gigabytes) in scale. A system to enable users to efficiently retrieve or query data from multiple databases simultaneously is needed. This research introduces a new, cloud-based query framework, designed and built using Language Integrated Query, to query existing data sources without the need to integrate or restructure existing databases. Protein data obtained through the query framework proves its feasibility and cost effectiveness.
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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16444/1/Michael_McGreevy_Thesis.pdf.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel ap
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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16444/.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel approa
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Miech, Antoine. "Large-scale learning from video and natural language." Electronic Thesis or Diss., Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLE059.

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Nous nous intéressons à l’apprentissage automatique d’algorithmes pour la compréhension automatique de vidéos. Une majorité des approches en compréhension de vidéos dépend de larges bases de données de vidéos manuellement annotées pour l’entraînement. Cependant, la collection et l’annotation de telles bases de données est fastidieuse, coûte cher et prend du temps. Pour palier à ce problème, cette thèse se concentre sur l’exploitation de large quantité d’annotations publiquement disponible, cependant bruitées, sous forme de langage naturel. En particulier, nous nous intéressons à un corpus dive
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Yao, Huan 1976. "Utterance verification in large vocabulary spoken language understanding system." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47633.

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Ealey, Douglas. "Natural language acquisition in large scale neural semantic networks." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310843.

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Books on the topic "Large Language"

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Amaratunga, Thimira. Understanding Large Language Models. Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.

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Martra, Pere. Large Language Models Projects. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0515-8.

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Marcondes, Francisco S., Adelino Gala, Renata Magalhães, Fernando Perez de Britto, Dalila Durães, and Paulo Novais. Natural Language Analytics with Generative Large-Language Models. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76631-2.

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Kucharavy, Andrei, Octave Plancherel, Valentin Mulder, Alain Mermoud, and Vincent Lenders, eds. Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7.

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Kamath, Uday, Kevin Keenan, Garrett Somers, and Sarah Sorenson. Large Language Models: A Deep Dive. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65647-7.

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Singh, Bhawna. Building Applications with Large Language Models. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0569-1.

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Armstrong, Susan, Kenneth Church, Pierre Isabelle, Sandra Manzi, Evelyne Tzoukermann, and David Yarowsky, eds. Natural Language Processing Using Very Large Corpora. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2390-9.

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B, Schnabel Robert, and Langley Research Center, eds. Flexible language constructs for large parallel programs. National Aeronautics and Space Administration, Langley Research Center, 1993.

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Sabandar, Jacob. Language learning in large classes in Indonesia. Lancaster-Leeds Language Learning in Large Classes Research Project, 1989.

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Susan, Armstrong, and Workshop on Very Large Corpora., eds. Natural language processing using very large corpora. Kluwer Academic, 1999.

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Book chapters on the topic "Large Language"

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Solanki, Shivam R., and Drupad K. Khublani. "Large Language Models." In Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8_5.

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Gunkel, David J. "Large Language Models." In AI for Communication. CRC Press, 2024. http://dx.doi.org/10.1201/9781003442240-5.

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Gunkel, David J. "Large Language Models." In Ethos, Technology, and AI in Contemporary Society. Routledge, 2024. http://dx.doi.org/10.4324/9781032688503-13.

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Taulli, Tom. "Large Language Models." In Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_5.

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McTear, Michael, and Marina Ashurkina. "Large Language Models." In Transforming Conversational AI. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_4.

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Vuppalapati, Chandrasekar. "Large Language Models." In International Series in Operations Research & Management Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56097-2_3.

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Gupta, Rajan, Sanju Tiwari, and Poonam Chaudhary. "Large Language Models." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82062-5_5.

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Desale, Ketan Sanjay. "Large Language Models." In Concept Drift in Large Language Models. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003595533-3.

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Ganguly, Arindam. "Large Language Models." In Scaling Enterprise Solutions with Large Language Models. Apress, 2025. https://doi.org/10.1007/979-8-8688-1154-8_4.

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Martra, Pere. "Natural Language to SQL." In Large Language Models Projects. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0515-8_6.

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Conference papers on the topic "Large Language"

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Csaki, Zoltan, Bo Li, Jonathan Lingjie Li, et al. "SambaLingo: Teaching Large Language Models New Languages." In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.mrl-1.1.

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Bishop, Cameron, Xiaodan Zhu, and Karen Rudie. "Large Language Model Translation of Indigenous Languages." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667295.

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Woźniak, Stanisław, Bartłomiej Koptyra, Arkadiusz Janz, Przemysław Kazienko, and Jan Kocoń. "Personalized Large Language Models." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00071.

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Norheim, Johannes J., and Eric Rebentisch. "Structuring Natural Language Requirements with Large Language Models." In 2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW). IEEE, 2024. http://dx.doi.org/10.1109/rew61692.2024.00013.

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Salau, Ayodeji Olalekan, Esuku Dickson Emmanuel, Ahmed Alemran, Chandra Kumar Dixit, and Sepiribo Lucky Braide. "Exploring Large Language Models for Natural Language Processing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877621.

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Goswami, Dhiman, Marcos Zampieri, Kai North, Shervin Malmasi, and Antonios Anastasopoulos. "Multilingual Native Language Identification with Large Language Models." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-srw.19.

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Zhang, Jiqiao, and Degen Huang. "Speech recognition for low-resource languages using large language models and related-language data." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070559.

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Pang, Jianhui, Fanghua Ye, Derek Wong, Xin He, Wanshun Chen, and Longyue Wang. "Anchor-based Large Language Models." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.295.

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Sanu, Erin, T. Keerthi Amudaa, Prasiddha Bhat, Guduru Dinesh, Apoorva Uday Kumar Chate, and Ramakanth Kumar P. "Limitations of Large Language Models." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10817070.

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Ke, Zixuan, Yifei Ming, and Shafiq Joty. "Adaptation of Large Language Models." In Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-tutorial.5.

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Reports on the topic "Large Language"

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Tranchero, Matteo, Cecil-Francis Brenninkmeijer, Arul Murugan, and Abhishek Nagaraj. Theorizing with Large Language Models. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w33033.

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Ludwig, Jens, Sendhil Mullainathan, and Ashesh Rambachan. Large Language Models: An Applied Econometric Framework. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33344.

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Humlum, Anders, and Emilie Vestergaard. Large Language Models, Small Labor Market Effects. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33777.

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Seymore, Kristie, and Ronald Rosenfeld. Large-Scale Topic Detection and Language Model Adaptation. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada327553.

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Zhang, Hao. Large Language Model (LLM) Monthly Report (2024 Apr). ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.0ps6xenm.

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Nelson, Paul. Natural Language Text Retrieval Using a Large Semantic Network. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274319.

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Prasad, Jayanti. Large Language Models: AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.

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This 5-day workshop provides a comprehensive understanding of large language models, their AI foundations, and applications in Python. Designed for PhD students, professors, and professional researchers, the seminar offers hands-on coding sessions, case studies, and discussions on the future of large language models in academic research.
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Link, Hamilton, Brandon Berman, Ryan Cooper, Haley Dodge, Nichole Fluke, and Robert VanCleave. Predictive Indicators of the Performance of Large Language Models. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480207.

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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Banco de España, 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses f
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Speed, Ann. Assessing the nature of large language models: A caution against anthropocentrism. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2429946.

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