Littérature scientifique sur le sujet « Large language models »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Large language models ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Large language models"
Cerf, Vinton G. « Large Language Models ». Communications of the ACM 66, no 8 (25 juillet 2023) : 7. http://dx.doi.org/10.1145/3606337.
Texte intégralSharma Shria Verma, Dhananjai. « Automated Penetration Testing using Large Language Models ». International Journal of Science and Research (IJSR) 13, no 4 (5 avril 2024) : 1826–31. http://dx.doi.org/10.21275/sr24427043741.
Texte intégralMishra, 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.
Texte intégralJain, Migul. « Future of Interacting with Computers and Large Language Models ». International Journal of Science and Research (IJSR) 12, no 10 (5 octobre 2023) : 1711–12. http://dx.doi.org/10.21275/sr231023121603.
Texte intégralNoever, David. « LARGE LANGUAGE MODELS FOR CIPHERS ». International Journal of Artificial Intelligence & ; Applications 14, no 03 (28 mai 2023) : 1–20. http://dx.doi.org/10.5121/ijaia.2023.14301.
Texte intégralD’Alessandro, William, Harry R. Lloyd et Nathaniel Sharadin. « Large Language Models and Biorisk ». American Journal of Bioethics 23, no 10 (3 octobre 2023) : 115–18. http://dx.doi.org/10.1080/15265161.2023.2250333.
Texte intégralShanahan, Murray. « Talking about Large Language Models ». Communications of the ACM 67, no 2 (25 janvier 2024) : 68–79. http://dx.doi.org/10.1145/3624724.
Texte intégralCheon, Hyundeuk. « Do Large Language Models Understand ? » CHUL HAK SA SANG : Journal of Philosophical Ideas 90 (30 novembre 2023) : 75–105. http://dx.doi.org/10.15750/chss.90.202311.003.
Texte intégralVeres, 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.
Texte intégralRoss, Angela, Kathleen McGrow, Degui Zhi et Laila Rasmy. « Foundation Models, Generative AI, and Large Language Models ». CIN : Computers, Informatics, Nursing 42, no 5 (mai 2024) : 377–87. http://dx.doi.org/10.1097/cin.0000000000001149.
Texte intégralThèses sur le sujet "Large language models"
Labeau, Matthieu. « Neural language models : Dealing with large vocabularies ». Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.
Texte intégralThis 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.
Texte intégralRecent 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/.
Texte intégralKropff, 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.
Texte intégralHittner, 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.
Texte intégralThesis advisor(s): Don Brutzman, Curt Blais. Includes bibliographical references (p. 117-118). Also available online.
Zhao, Ying, et 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.
Texte intégralPan, Bi-Yu. « Hierarchical test generation for VHDL behavioral models ». Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09052009-040449/.
Texte intégralWest, 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.
Texte intégralDepartment 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/.
Texte intégralNarayanaswamy, 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/.
Texte intégralLivres sur le sujet "Large language models"
Amaratunga, Thimira. Understanding Large Language Models. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.
Texte intégralTörnberg, 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.
Texte intégralA, Croon Marcel, et Hagenaars Jacques A, dir. Marginal models : For dependent, clustered, and longitudinal categorical data. New York : Springer, 2009.
Trouver le texte intégralSatō, 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.
Trouver le texte intégralBashkatov, Alexander. Modeling in OpenSCAD : examples. ru : INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/959073.
Texte intégralGreasidis, Thodoris. jQuery design patterns : Learn the best practices on writing efficient jQuery applications to maximize performance in large-scale deployments. Birmingham : Packt Publishing, 2016.
Trouver le texte intégralRobitaille, France, et Marjorie Perreault. Alizé : Vent du large 3. Montréal : Groupe Beauchemin, 2007.
Trouver le texte intégralVavrenyuk, Aleksandr, Viktor Makarov et Stanislav Kutepov. Operating systems. UNIX bases. ru : INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/11186.
Texte intégralKublik, Sandra, et Shubham Saboo. Gpt-3 : Building Innovative NLP Products Using Large Language Models. O'Reilly Media, Incorporated, 2022.
Trouver le texte intégralAshwin, Julian, Aditya Chhabra et 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.
Texte intégralChapitres de livres sur le sujet "Large language models"
McTear, Michael, et Marina Ashurkina. « Large Language Models ». Dans Transforming Conversational AI, 61–84. Berkeley, CA : Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_4.
Texte intégralTaulli, Tom. « Large Language Models ». Dans Generative AI, 93–125. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_5.
Texte intégralVuppalapati, Chandrasekar. « Large Language Models ». Dans 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.
Texte intégralAmaratunga, Thimira. « What Makes LLMs Large ? » Dans Understanding Large Language Models, 81–117. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_4.
Texte intégralAmaratunga, Thimira. « Transformers ». Dans Understanding Large Language Models, 55–79. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_3.
Texte intégralAmaratunga, Thimira. « NLP Through the Ages ». Dans Understanding Large Language Models, 9–54. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_2.
Texte intégralAmaratunga, Thimira. « Introduction ». Dans Understanding Large Language Models, 1–7. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_1.
Texte intégralAmaratunga, Thimira. « Popular LLMs ». Dans Understanding Large Language Models, 119–30. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_5.
Texte intégralAmaratunga, Thimira. « Threats, Opportunities, and Misconceptions ». Dans Understanding Large Language Models, 131–48. Berkeley, CA : Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_6.
Texte intégralPires, Ramon, Hugo Abonizio, Thales Sales Almeida et Rodrigo Nogueira. « Sabiá : Portuguese Large Language Models ». Dans Intelligent Systems, 226–40. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45392-2_15.
Texte intégralActes de conférences sur le sujet "Large language models"
Bariah, Lina, Hang Zou, Qiyang Zhao, Belkacem Mouhouche, Faouzi Bader et Merouane Debbah. « Understanding Telecom Language Through Large Language Models ». Dans GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437725.
Texte intégralWitteveen, Sam, et Martin Andrews. « Paraphrasing with Large Language Models ». Dans 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.
Texte intégralDebbah, Mérouane. « Large Language Models for Telecom ». Dans 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2023. http://dx.doi.org/10.1109/fmec59375.2023.10305960.
Texte intégralKoyejo, Sanmi, et Bo Li. « Towards Trustworthy Large Language Models ». Dans 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.
Texte intégralKodali, Ravi Kishore, Yatendra Prasad Upreti et Lakshmi Boppana. « Large Language Models in AWS ». Dans 2024 1st International Conference on Robotics, Engineering, Science, and Technology (RESTCON). IEEE, 2024. http://dx.doi.org/10.1109/restcon60981.2024.10463557.
Texte intégralMadasu, Avinash, et Shashank Srivastava. « What do Large Language Models Learn beyond Language ? » Dans 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.
Texte intégralZhou, Chunfang, Qingyue Gong, Jinyang Zhu et 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 ». Dans 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.
Texte intégralDeng, Yinlin, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang et Lingming Zhang. « Large Language Models Are Zero-Shot Fuzzers : Fuzzing Deep-Learning Libraries via Large Language Models ». Dans 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.
Texte intégralYang, Yueting, Xintong Zhang, Jinan Xu et Wenjuan Han. « Empowering Vision-Language Models for Reasoning Ability through Large Language Models ». Dans ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446407.
Texte intégralTodd, Graham, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green et Julian Togelius. « Level Generation Through Large Language Models ». Dans FDG 2023 : Foundations of Digital Games 2023. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3582437.3587211.
Texte intégralRapports d'organisations sur le sujet "Large language models"
Prasad, Jayanti. Large Language Models : AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.
Texte intégralAlonso-Robisco, Andres, et Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid : Banco de España, août 2023. http://dx.doi.org/10.53479/33412.
Texte intégralMarra de Artiñano, Ignacio, Franco Riottini Depetris et Christian Volpe Martincus. Automatic Product Classification in International Trade : Machine Learning and Large Language Models. Inter-American Development Bank, juillet 2023. http://dx.doi.org/10.18235/0005012.
Texte intégralHorton, John. Large Language Models as Simulated Economic Agents : What Can We Learn from Homo Silicus ? Cambridge, MA : National Bureau of Economic Research, avril 2023. http://dx.doi.org/10.3386/w31122.
Texte intégralGluckman, Peter, et Hema Sridhar. A framework for evaluating rapidly developing digital and related technologies : AI, Large Language Models and beyond. International Science Council, octobre 2023. http://dx.doi.org/10.24948/2023.11.
Texte intégralGluckman, Peter, et Hema Sridhar. A guide for policy-makers : Evaluating rapidly developing technologies including AI, large language models and beyond. International Science Council, avril 2024. http://dx.doi.org/10.24948/2024.07.
Texte intégralZhu, Minjie, et Michael Scott. Fluid-Structure Interaction and Python-Scripting Capabilities in OpenSees. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, août 2019. http://dx.doi.org/10.55461/vdix3057.
Texte intégralSeymore, Kristie, et Ronald Rosenfeld. Large-Scale Topic Detection and Language Model Adaptation. Fort Belvoir, VA : Defense Technical Information Center, juin 1997. http://dx.doi.org/10.21236/ada327553.
Texte intégralZhang, Hao. Large Language Model (LLM) Monthly Report (2024 Apr). ResearchHub Technologies, Inc., mai 2024. http://dx.doi.org/10.55277/researchhub.0ps6xenm.
Texte intégralFischer, Eric, Rebecca McCaughrin, Saketh Prazad et Mark Vandergon. Fed Transparency and Policy Expectation Errors : A Text Analysis Approach. Federal Reserve Bank of New York, novembre 2023. http://dx.doi.org/10.59576/sr.1081.
Texte intégral