Literatura académica sobre el tema "Large language models"
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Artículos de revistas sobre el tema "Large language models"
Cerf, Vinton G. "Large Language Models". Communications of the ACM 66, n.º 8 (25 de julio de 2023): 7. http://dx.doi.org/10.1145/3606337.
Texto completoSharma Shria Verma, Dhananjai. "Automated Penetration Testing using Large Language Models". International Journal of Science and Research (IJSR) 13, n.º 4 (5 de abril de 2024): 1826–31. http://dx.doi.org/10.21275/sr24427043741.
Texto completoMishra, Vinaytosh. "Large Language Models in Medical Education and Quality Concerns". Journal of Quality in Health Care & Economics 6, n.º 1 (2023): 1–3. http://dx.doi.org/10.23880/jqhe-16000319.
Texto completoJain, Migul. "Future of Interacting with Computers and Large Language Models". International Journal of Science and Research (IJSR) 12, n.º 10 (5 de octubre de 2023): 1711–12. http://dx.doi.org/10.21275/sr231023121603.
Texto completoNoever, David. "LARGE LANGUAGE MODELS FOR CIPHERS". International Journal of Artificial Intelligence & Applications 14, n.º 03 (28 de mayo de 2023): 1–20. http://dx.doi.org/10.5121/ijaia.2023.14301.
Texto completoD’Alessandro, William, Harry R. Lloyd y Nathaniel Sharadin. "Large Language Models and Biorisk". American Journal of Bioethics 23, n.º 10 (3 de octubre de 2023): 115–18. http://dx.doi.org/10.1080/15265161.2023.2250333.
Texto completoShanahan, Murray. "Talking about Large Language Models". Communications of the ACM 67, n.º 2 (25 de enero de 2024): 68–79. http://dx.doi.org/10.1145/3624724.
Texto completoCheon, Hyundeuk. "Do Large Language Models Understand?" CHUL HAK SA SANG : Journal of Philosophical Ideas 90 (30 de noviembre de 2023): 75–105. http://dx.doi.org/10.15750/chss.90.202311.003.
Texto completoVeres, Csaba. "Large Language Models are Not Models of Natural Language: They are Corpus Models". IEEE Access 10 (2022): 61970–79. http://dx.doi.org/10.1109/access.2022.3182505.
Texto completoRoss, Angela, Kathleen McGrow, Degui Zhi y Laila Rasmy. "Foundation Models, Generative AI, and Large Language Models". CIN: Computers, Informatics, Nursing 42, n.º 5 (mayo de 2024): 377–87. http://dx.doi.org/10.1097/cin.0000000000001149.
Texto completoTesis sobre el tema "Large language models"
Labeau, Matthieu. "Neural language models : Dealing with large vocabularies". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.
Texto completoThis work investigates practical methods to ease training and improve performances of neural language models with large vocabularies. The main limitation of neural language models is their expensive computational cost: it depends on the size of the vocabulary, with which it grows linearly. Despite several training tricks, the most straightforward way to limit computation time is to limit the vocabulary size, which is not a satisfactory solution for numerous tasks. Most of the existing methods used to train large-vocabulary language models revolve around avoiding the computation of the partition function, ensuring that output scores are normalized into a probability distribution. Here, we focus on sampling-based approaches, including importance sampling and noise contrastive estimation. These methods allow an approximate computation of the partition function. After examining the mechanism of self-normalization in noise-contrastive estimation, we first propose to improve its efficiency with solutions that are adapted to the inner workings of the method and experimentally show that they considerably ease training. Our second contribution is to expand on a generalization of several sampling based objectives as Bregman divergences, in order to experiment with new objectives. We use Beta divergences to derive a set of objectives from which noise contrastive estimation is a particular case. Finally, we aim at improving performances on full vocabulary language models, by augmenting output words representation with subwords. We experiment on a Czech dataset and show that using character-based representations besides word embeddings for output representations gives better results. We also show that reducing the size of the output look-up table improves results even more
Zervakis, Georgios. "Enriching large language models with semantic lexicons and analogies". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0039.
Texto completoRecent advances in deep learning and neural networks have made it possible to address complex natural language processing tasks, which find application in a plethora of real-world problems ranging from smart assistants in mobile devices to the prediction of cancer. Nonetheless, modern systems based on these frameworks exhibit various limitations that may compromise their performance and trustworthiness, render them unfair towards minorities, or subject them to privacy leakage. It is our belief that integrating symbolic knowledge and reasoning into the deep learning framework is a necessary step towards addressing the aforementioned limitations. For example, lexical resources can enrich deep neural networks with semantic or syntactic knowledge, and logical rules can provide learning and reasoning mechanisms. Therefore, the scope of this thesis is to develop and evaluate ways of integrating different types of symbolic knowledge and reasoning into a widely used language model, Bidirectional Encoder Representations from Transformers (BERT). ln a first stage, we consider retrofitting, a simple and popular technique for refining distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating this knowledge into BERT contextualized embeddings. We evaluate these methods on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis, and show that they do not substantially impact the performance for these tasks. Furthermore, we conduct a qualitative analysis to provide further insights on this negative result. ln a second stage, we integrate analogical reasoning with BERT as a means to improve its performance on the target sense verification task, and make it more robust. To do so, we reformulate target sense verification as an analogy detection task. We present a hybrid model that combines BERT to encode the input data into quadruples and a convolutional neural classifier to decide whether they constitute valid analogies. We test our system on a benchmark dataset, and show that it can outperform existing approaches. Our empirical study shows the importance of the input encoding for BERT, and how this dependence gets alleviated by integrating the axiomatic properties of analogies during training, while preserving performance and improving robustness
Chadha, Vikrampal. "Simulation of large-scale system-level models". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12162009-020334/.
Texto completoKropff, Emilio. "Statistical and dynamical properties of large cortical network models: insights into semantic memory and language". Doctoral thesis, SISSA, 2007. http://hdl.handle.net/20.500.11767/4639.
Texto completoHittner, Brian Edward. "Rendering large-scale terrain models and positioning objects in relation to 3D terrain". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FHittner.pdf.
Texto completoThesis advisor(s): Don Brutzman, Curt Blais. Includes bibliographical references (p. 117-118). Also available online.
Zhao, Ying y ying zhao@rmit edu au. "Effective Authorship Attribution in Large Document Collections". RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080730.162501.
Texto completoPan, Bi-Yu. "Hierarchical test generation for VHDL behavioral models". Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09052009-040449/.
Texto completoWest, James F. "An examination of the application of design metrics to the development of testing strategies in large-scale SDL models". Virtual Press, 2000. http://liblink.bsu.edu/uhtbin/catkey/1191725.
Texto completoDepartment of Computer Science
Kapoor, Shekhar. "Process level test generation for VHDL behavioral models". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-05022009-040753/.
Texto completoNarayanaswamy, Sathyanarayanan. "Development of VHDL behavioral models with back annotated timing". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06112009-063442/.
Texto completoLibros sobre el tema "Large language models"
Amaratunga, Thimira. Understanding Large Language Models. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.
Texto completoTörnberg, Petter. How to Use Large-Language Models for Text Analysis. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2024. http://dx.doi.org/10.4135/9781529683707.
Texto completoA, Croon Marcel y Hagenaars Jacques A, eds. Marginal models: For dependent, clustered, and longitudinal categorical data. New York: Springer, 2009.
Buscar texto completoSatō, Hideto. A data model, knowledge base, and natural language processing for sharing a large statistical database. Ibaraki, Osaka, Japan: Institute of Social and Economic Research, Osaka University, 1989.
Buscar texto completoBashkatov, Alexander. Modeling in OpenSCAD: examples. ru: INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/959073.
Texto completojQuery design patterns: Learn the best practices on writing efficient jQuery applications to maximize performance in large-scale deployments. Birmingham: Packt Publishing, 2016.
Buscar texto completoAlizé: Vent du large 3. Montréal: Groupe Beauchemin, 2007.
Buscar texto completoVavrenyuk, Aleksandr, Viktor Makarov y Stanislav Kutepov. Operating systems. UNIX bases. ru: INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/11186.
Texto completoGpt-3: Building Innovative NLP Products Using Large Language Models. O'Reilly Media, Incorporated, 2022.
Buscar texto completoAshwin, Julian, Aditya Chhabra y Vijayendra Rao. Using Large Language Models for Qualitative Analysis can Introduce Serious Bias. World Bank Washington, DC, 2023. http://dx.doi.org/10.1596/1813-9450-10597.
Texto completoCapítulos de libros sobre el tema "Large language models"
McTear, Michael y Marina Ashurkina. "Large Language Models". En Transforming Conversational AI, 61–84. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_4.
Texto completoTaulli, Tom. "Large Language Models". En Generative AI, 93–125. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_5.
Texto completoVuppalapati, Chandrasekar. "Large Language Models". En International Series in Operations Research & Management Science, 71–131. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56097-2_3.
Texto completoAmaratunga, Thimira. "What Makes LLMs Large?" En Understanding Large Language Models, 81–117. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_4.
Texto completoAmaratunga, Thimira. "Transformers". En Understanding Large Language Models, 55–79. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_3.
Texto completoAmaratunga, Thimira. "NLP Through the Ages". En Understanding Large Language Models, 9–54. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_2.
Texto completoAmaratunga, Thimira. "Introduction". En Understanding Large Language Models, 1–7. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_1.
Texto completoAmaratunga, Thimira. "Popular LLMs". En Understanding Large Language Models, 119–30. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_5.
Texto completoAmaratunga, Thimira. "Threats, Opportunities, and Misconceptions". En Understanding Large Language Models, 131–48. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_6.
Texto completoPires, Ramon, Hugo Abonizio, Thales Sales Almeida y Rodrigo Nogueira. "Sabiá: Portuguese Large Language Models". En Intelligent Systems, 226–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45392-2_15.
Texto completoActas de conferencias sobre el tema "Large language models"
Bariah, Lina, Hang Zou, Qiyang Zhao, Belkacem Mouhouche, Faouzi Bader y Merouane Debbah. "Understanding Telecom Language Through Large Language Models". En GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437725.
Texto completoWitteveen, Sam y Martin Andrews. "Paraphrasing with Large Language Models". En Proceedings of the 3rd Workshop on Neural Generation and Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5623.
Texto completoDebbah, Mérouane. "Large Language Models for Telecom". En 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2023. http://dx.doi.org/10.1109/fmec59375.2023.10305960.
Texto completoKoyejo, Sanmi y Bo Li. "Towards Trustworthy Large Language Models". En WSDM '24: The 17th ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3616855.3636454.
Texto completoKodali, Ravi Kishore, Yatendra Prasad Upreti y Lakshmi Boppana. "Large Language Models in AWS". En 2024 1st International Conference on Robotics, Engineering, Science, and Technology (RESTCON). IEEE, 2024. http://dx.doi.org/10.1109/restcon60981.2024.10463557.
Texto completoMadasu, Avinash y Shashank Srivastava. "What do Large Language Models Learn beyond Language?" En Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.516.
Texto completoZhou, Chunfang, Qingyue Gong, Jinyang Zhu y Huidan Luan. "Research and Application of Large Language Models in HealthcareCurrent Development of Large Language Models in the Healthcare FieldA Framework for Applying Large Language Models and the Opportunities and Challenges of Large Language Models in Healthcare: A Framework for Applying Large Language Models and the Opportunities and Challenges of Large Language Models in Healthcare". En ISAIMS 2023: 2023 4th International Symposium on Artificial Intelligence for Medicine Science. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3644116.3644226.
Texto completoDeng, Yinlin, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang y Lingming Zhang. "Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models". En ISSTA '23: 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3597926.3598067.
Texto completoYang, Yueting, Xintong Zhang, Jinan Xu y Wenjuan Han. "Empowering Vision-Language Models for Reasoning Ability through Large Language Models". En ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446407.
Texto completoTodd, Graham, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green y Julian Togelius. "Level Generation Through Large Language Models". En FDG 2023: Foundations of Digital Games 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3582437.3587211.
Texto completoInformes sobre el tema "Large language models"
Prasad, Jayanti. Large Language Models: AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.
Texto completoAlonso-Robisco, Andres y Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid: Banco de España, agosto de 2023. http://dx.doi.org/10.53479/33412.
Texto completoMarra de Artiñano, Ignacio, Franco Riottini Depetris y Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, julio de 2023. http://dx.doi.org/10.18235/0005012.
Texto completoHorton, John. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? Cambridge, MA: National Bureau of Economic Research, abril de 2023. http://dx.doi.org/10.3386/w31122.
Texto completoGluckman, Peter y Hema Sridhar. A framework for evaluating rapidly developing digital and related technologies: AI, Large Language Models and beyond. International Science Council, octubre de 2023. http://dx.doi.org/10.24948/2023.11.
Texto completoGluckman, Peter y Hema Sridhar. A guide for policy-makers: Evaluating rapidly developing technologies including AI, large language models and beyond. International Science Council, abril de 2024. http://dx.doi.org/10.24948/2024.07.
Texto completoZhu, Minjie y Michael Scott. Fluid-Structure Interaction and Python-Scripting Capabilities in OpenSees. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, agosto de 2019. http://dx.doi.org/10.55461/vdix3057.
Texto completoSeymore, Kristie y Ronald Rosenfeld. Large-Scale Topic Detection and Language Model Adaptation. Fort Belvoir, VA: Defense Technical Information Center, junio de 1997. http://dx.doi.org/10.21236/ada327553.
Texto completoZhang, Hao. Large Language Model (LLM) Monthly Report (2024 Apr). ResearchHub Technologies, Inc., mayo de 2024. http://dx.doi.org/10.55277/researchhub.0ps6xenm.
Texto completoFischer, Eric, Rebecca McCaughrin, Saketh Prazad y Mark Vandergon. Fed Transparency and Policy Expectation Errors: A Text Analysis Approach. Federal Reserve Bank of New York, noviembre de 2023. http://dx.doi.org/10.59576/sr.1081.
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