Relevant bibliographies by topics / Language models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Language models'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Language models"

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Li, Hang. "Language models." Communications of the ACM 65, no. 7 (2022): 56–63. http://dx.doi.org/10.1145/3490443.

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Begnarovich, Uralov Azamat. "The Inconsistency Of Language Models." American Journal of Social Science and Education Innovations 03, no. 09 (2021): 39–44. http://dx.doi.org/10.37547/tajssei/volume03issue09-09.

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The article deals with the problem of disproportion in morpheme units of linguistics and patterns. Based on the disproportion, information is given on the combined affixes formed in the morphemes, the expanded forms, and the analytic and synthetic forms. The data is based on the opinions of the world's leading linguists. The ideas are proven using examples. The formation of a particular linguistic model is a disproportion in the language system (meaning-function-methodological features): confusion of meanings, multifunctionality, semantics, competition in the use of forms (one form has more an
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Shimi, G., C. Jerin Mahibha, and Durairaj Thenmozhi. "An Empirical Analysis of Language Detection in Dravidian Languages." Indian Journal Of Science And Technology 17, no. 15 (2024): 1515–26. http://dx.doi.org/10.17485/ijst/v17i15.765.

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Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches wi
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Mezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.095762.

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Mezzoudj, Freha, and Abdelkader Benyettou. "An empirical study of statistical language models: n-gram language models vs. neural network language models." International Journal of Innovative Computing and Applications 9, no. 4 (2018): 189. http://dx.doi.org/10.1504/ijica.2018.10016827.

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Babb, Robert G. "Language and models." ACM SIGSOFT Software Engineering Notes 13, no. 1 (1988): 43–45. http://dx.doi.org/10.1145/43857.43872.

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Liu, X., M. J. F. Gales, and P. C. Woodland. "Paraphrastic language models." Computer Speech & Language 28, no. 6 (2014): 1298–316. http://dx.doi.org/10.1016/j.csl.2014.04.004.

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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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Nederhof, Mark-Jan. "A General Technique to Train Language Models on Language Models." Computational Linguistics 31, no. 2 (2005): 173–85. http://dx.doi.org/10.1162/0891201054223986.

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We show that under certain conditions, a language model can be trained on the basis of a second language model. The main instance of the technique trains a finite automaton on the basis of a probabilistic context-free grammar, such that the Kullback-Leibler distance between grammar and trained automaton is provably minimal. This is a substantial generalization of an existing algorithm to train an n-gram model on the basis of a probabilistic context-free grammar.
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Veres, 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.

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Dissertations / Theses on the topic "Language models"

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Livingstone, Daniel Jack. "Computer models of the evolution of language and languages." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398331.

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Laurençon, Hugo. "Foundation Vision-Language models." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS004.

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Ces dernières années, les grands modèles de langage (LLMs) ont montré des performances remarquables sur des tâches variées et ont commencé à être largement intégrés dans diverses applications. Les modèles vision-langage (VLMs), qui étendent les LLMs en incorporant en plus la compréhension d'images, offrent un potentiel supplémentaire. Ces modèles pourraient transformer des domaines tels que le développement web en traduisant des captures d'écran de pages web en code, faciliter la recherche d'informations via une navigation web autonome, et automatiser des tâches comme la classification et la s
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Ryder, Robin Jeremy. "Phylogenetic models of language diversification." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543009.

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Waegner, Nicholas Paul. "Stochastic models for language acquisition." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309214.

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Niesler, Thomas Richard. "Category-based statistical language models." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627372.

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Wallach, Hanna Megan. "Structured topic models for language." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612547.

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Douzon, Thibault. "Language models for document understanding." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0075.

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Chaque jour, les entreprises du monde entier reçoivent et traitent d'énormes volumes de documents, entraînant des coûts considérables. Pour réduire ces coûts, de grandes entreprises automatisent le traitement documentaire, visant une automatisation complète. Cette thèse se concentre sur l'utilisation de modèles d'apprentissage machine pour extraire des informations de documents. Les progrès récents en matière d'architecture de modèle, en particulier les transformeurs, ont révolutionné le domaine grâce à leur utilisation généralisée de l'attention et à l'amélioration des pré-entraînements auto-
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Townsend, Duncan Clarke McIntire. "Using a symbolic language parser to Improve Markov language models." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100621.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 31-32).<br>This thesis presents a hybrid approach to natural language processing that combines an n-gram (Markov) model with a symbolic parser. In concert these two techniques are applied to the problem of sentence simplific
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Buttery, P. J. "Computational models for first language acquisition." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597195.

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This work investigates a computational model of first language acquisition; the Categorical Grammar Learner or CGL. The model builds on the work of Villavicenio, who created a parametric Categorical Grammar learner that organises its parameters into an inheritance hierarchy, and also on the work of Buszkowski and Kanazawa, who demonstrated the learnability of a <i>k</i>-valued Classic Categorial Grammar (which uses only the rules of function application) from strings. The CGL is able to learn a <i>k</i>-valued General Categorial Grammar (which uses the rules of function application, function c
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Nkadimeng, Calvin. "Language identification using Gaussian mixture models." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4170.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: The importance of Language Identification for African languages is seeing a dramatic increase due to the development of telecommunication infrastructure and, as a result, an increase in volumes of data and speech traffic in public networks. By automatically processing the raw speech data the vital assistance given to people in distress can be speeded up, by referring their calls to a person knowledgeable in that language. To this effect a speech corpus was developed and various
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Books on the topic "Language models"

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Stevenson, Rosemary J. Models of language development. Open University Press, 1988.

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Ann, Ryan, Wray Alison, and British Association for Applied Linguistics., eds. Evolving models of language. British Association for Applied Linguistics in association with Multilingual Matters, 1997.

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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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1945-, Giora Rachel, ed. Models of figurative language. Lawrence Erlbaum Association, 2001.

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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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Meduna, Alexander, and Ondřej Soukup. Modern Language Models and Computation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63100-4.

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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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1950-, Barlow Michael, and Kemmer Suzanne, eds. Usage-based models of language. CSLI Publications, Center for the Study of Language and Information, 1999.

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Przystalski, Karol, Jan K. Argasiński, Natalia Lipp, and Dawid Pacholczyk. Building Personality-Driven Language Models. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80087-0.

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Book chapters on the topic "Language models"

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Hiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_923-2.

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Hiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_923.

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Tanaka-Ishii, Kumiko. "Language Models." In Mathematics in Mind. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59377-3_17.

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Hiemstra, Djoerd. "Language Models." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_923.

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Moorkens, Joss, Andy Way, and Séamus Lankford. "Large Language Models and Multilingual Language Models." In Automating Translation. Routledge, 2024. http://dx.doi.org/10.4324/9781003381280-10.

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Turner, Raymond. "Programming Language Specification." In Computable Models. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-052-4_22.

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Mark, Kevin E., Michael I. Miller, and Ulf Grenander. "Constrained Stochastic Language Models." In Image Models (and their Speech Model Cousins). Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4056-3_7.

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Skansi, Sandro. "Neural Language Models." In Undergraduate Topics in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2_9.

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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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Conference papers on the topic "Language models"

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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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Plenz, Moritz, and Anette Frank. "Graph Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.245.

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Liu, Shihong, Samuel Yu, Zhiqiu Lin, Deepak Pathak, and Deva Ramanan. "Language Models as Black-Box Optimizers for Vision-Language Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01206.

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Kim, Minsu, and James Thorne. "Epistemology of Language Models: Do Language Models Have Holistic Knowledge?" 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.751.

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Ginn, Michael, Mans Hulden, and Alexis Palmer. "Can we teach language models to gloss endangered languages?" In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.337.

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Xu, Jiarui, Xingyi Zhou, Shen Yan, et al. "Pixel Aligned Language Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01238.

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Kallini, Julie, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, and Christopher Potts. "Mission: Impossible Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.787.

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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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Chen, Chi, Yiyang Du, Zheng Fang, et al. "Model Composition for Multimodal Large Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.606.

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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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Reports on the topic "Language models"

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Seymore, Kristie, and Ronald Rosenfeld. Scalable Trigram Backoff Language Models,. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada310721.

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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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Hacioglu, Kadri, and Wayne Ward. On Combining Language Models: Oracle Approach. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada460991.

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Lavrenko, Victor. Localized Smoothing for Multinomial Language Models. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada477851.

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Raimondo, S., T. Chen, A. Zakharov, et al. Datasets to support geological language models. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329265.

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Howland, Scott, Jessica Yaros, and Noriaki Kono. MetaText: Compositional Generalization in Deep Language Models. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1987883.

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Cullen, Cabot, and Zhehui Wang. Exploration of Language Models for ICF Design. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1992244.

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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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Buchanan, Ben, Andrew Lohn, Micah Musser, and Katerina Sedova. Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2021ca003.

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Growing popular and industry interest in high-performing natural language generation models has led to concerns that such models could be used to generate automated disinformation at scale. This report examines the capabilities of GPT-3--a cutting-edge AI system that writes text--to analyze its potential misuse for disinformation. A model like GPT-3 may be able to help disinformation actors substantially reduce the work necessary to write disinformation while expanding its reach and potentially also its effectiveness.
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