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Journal articles on the topic 'Language models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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6

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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9

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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Xiao, Jingxuan, and Jiawei Wu. "Transfer Learning for Cross-Language Natural Language Processing Models." Journal of Computer Technology and Applied Mathematics 1, no. 3 (2024): 30–38. https://doi.org/10.5281/zenodo.13366733.

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Cross-language natural language processing (NLP) presents numerous challenges due to the wide array of linguistic structures and vocabulary found within each language. Transfer learning has proven itself successful at meeting these challenges by drawing upon knowledge gained in highly resourced languages to enhance performance in lower resource ones. This paper investigates the application of transfer learning in cross-language NLP, exploring various methodologies, models and their efficacy. More specifically, we investigate mechanisms related to model adaptation, fine-tuning techniques and in

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Alam, Salman. "Comparison of Various Models in the Context of Language Identification (Indo Aryan Languages)." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 185–88. https://doi.org/10.21275/sr21303115028.

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13

Amusa, Kamoli Akinwale, Tolulope Christiana Erinosho, Olufunke Olubusola Nuga, and Abdulmatin Olalekan Omotoso. "YorubaAI: Bridging Language Barrier with Advanced Language Models." Journal of Applied Artificial Intelligence 6, no. 1 (2025): 39–52. https://doi.org/10.48185/jaai.v6i1.1474.

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YorubaAI addresses the digital divide caused by language barriers, particularly for Yoruba language speakers who struggle to interact with advanced large language models (LLMs) like GPT-4, which primarily support high-resource languages. This study develops a system, named YorubaAI, for seamless communication in Yoruba language with LLMs. The YorubaAI enables users to input and receive responses in Yoruba language, both in text and audio formats. To achieve this, a speech-to-text (STT) model is fine-tuned for automatic Yoruba language speech recognition while a text-to-speech (TTS) model is em

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14

Mitra, Arijita, Nasim Ahmed, Payel Pramanik, and Sayantan Nandi. "Language Studies and Communication Models." International Journal of English Learning & Teaching Skills 3, no. 1 (2020): 1776–94. http://dx.doi.org/10.15864/ijelts.3110.

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Language studies and communication is very important and precisely used in our daily lives. It’s not just about the grammar but learning language means learning expressions, learning about people and their culture. Language represents words when communication is verbal or written. We can conclude that Language is a method of Communication. The aim to put up this topic was to highlight the momentousness of communication on our life which can be achieved through the knowledge acquired by the study of languages. Shaping one’s ideas into reality requires proper transmission of idea which is where

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Liu, Xunying, James L. Hieronymus, Mark J. F. Gales, and Philip C. Woodland. "Syllable language models for Mandarin speech recognition: Exploiting character language models." Journal of the Acoustical Society of America 133, no. 1 (2013): 519–28. http://dx.doi.org/10.1121/1.4768800.

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De Coster, Mathieu, and Joni Dambre. "Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation." Information 13, no. 5 (2022): 220. http://dx.doi.org/10.3390/info13050220.

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We consider neural sign language translation: machine translation from signed to written languages using encoder–decoder neural networks. Translating sign language videos to written language text is especially complex because of the difference in modality between source and target language and, consequently, the required video processing. At the same time, sign languages are low-resource languages, their datasets dwarfed by those available for written languages. Recent advances in written language processing and success stories of transfer learning raise the question of how pretrained written

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O'Rourke, Bernadette. "Language Revitalisation Models in Minority Language Contexts." Anthropological Journal of European Cultures 24, no. 1 (2015): 63–82. http://dx.doi.org/10.3167/ajec.2015.240105.

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This article looks at the historicisation of the native speaker and ideologies of authenticity and anonymity in Europe's language revitalisation movements. It focuses specifically on the case of Irish in the Republic of Ireland and examines how the native speaker ideology and the opposing ideological constructs of authenticity and anonymity filter down to the belief systems and are discursively produced by social actors on the ground. For this I draw on data from ongoing fieldwork in the Republic of Ireland, drawing on interviews with a group of Irish language enthusiasts located outside the o

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Wu, Zhaofeng, William Merrill, Hao Peng, Iz Beltagy, and Noah A. Smith. "Transparency Helps Reveal When Language Models Learn Meaning." Transactions of the Association for Computational Linguistics 11 (2023): 617–34. http://dx.doi.org/10.1162/tacl_a_00565.

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Abstract Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations (i.e., languages with strong transparency), both autoregressive and masked language models successfully learn to emulate semantic relations between expressions. However, when denotations are changed to be context-dependent with the language otherwise unmodified, th

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G, Sajini. "Computational Evaluation of Language Models by Considering Various Scaling Properties for Processing Natural Languages." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 691–700. http://dx.doi.org/10.5373/jardcs/v12sp7/20202159.

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Tsvetkov, Yulia, and Chris Dyer. "Cross-Lingual Bridges with Models of Lexical Borrowing." Journal of Artificial Intelligence Research 55 (January 13, 2016): 63–93. http://dx.doi.org/10.1613/jair.4786.

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Linguistic borrowing is the phenomenon of transferring linguistic constructions (lexical, phonological, morphological, and syntactic) from a donor language to a recipient language as a result of contacts between communities speaking different languages. Borrowed words are found in all languages, andin contrast to cognate relationshipsborrowing relationships may exist across unrelated languages (for example, about 40% of Swahilis vocabulary is borrowed from the unrelated language Arabic). In this work, we develop a model of morpho-phonological transformations across languages. Its featur

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Hayashi, Hiroaki, Zecong Hu, Chenyan Xiong, and Graham Neubig. "Latent Relation Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7911–18. http://dx.doi.org/10.1609/aaai.v34i05.6298.

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In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entities that occur therein via knowledge graph relations. This model has a number of attractive properties: it not only improves language modeling performance, but is also able to annotate the posterior probability of entity spans for a given text through relations. Experiments demonstrate empirical improvements over both word-based language models and a previous approach that incorporates knowledge graph information. Qual

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Yogatama, Dani, Cyprien de Masson d’Autume, and Lingpeng Kong. "Adaptive Semiparametric Language Models." Transactions of the Association for Computational Linguistics 9 (2021): 362–73. http://dx.doi.org/10.1162/tacl_a_00371.

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Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination

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23

Cockburn, Alexander, and Noam Chomsky. "Models, Nature, and Language." Grand Street, no. 50 (1994): 170. http://dx.doi.org/10.2307/25007794.

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24

Lavrenko, Victor, and W. Bruce Croft. "Relevance-Based Language Models." ACM SIGIR Forum 51, no. 2 (2017): 260–67. http://dx.doi.org/10.1145/3130348.3130376.

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Buckman, Jacob, and Graham Neubig. "Neural Lattice Language Models." Transactions of the Association for Computational Linguistics 6 (December 2018): 529–41. http://dx.doi.org/10.1162/tacl_a_00036.

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In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions — including polysemy and the existence of multiword lexical items — into our language model. Experiments on multiple language modeling tasks show that English neu

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26

Varona, A., and I. Torres. "Scaling Smoothed Language Models." International Journal of Speech Technology 8, no. 4 (2005): 341–61. http://dx.doi.org/10.1007/s10772-006-9047-5.

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Schwenk, Holger. "Continuous space language models." Computer Speech & Language 21, no. 3 (2007): 492–518. http://dx.doi.org/10.1016/j.csl.2006.09.003.

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28

Bengio, Yoshua. "Neural net language models." Scholarpedia 3, no. 1 (2008): 3881. http://dx.doi.org/10.4249/scholarpedia.3881.

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29

Schütze, Hinrich, and Michael Walsh. "Half-Context Language Models." Computational Linguistics 37, no. 4 (2011): 843–65. http://dx.doi.org/10.1162/coli_a_00078.

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This article investigates the effects of different degrees of contextual granularity on language model performance. It presents a new language model that combines clustering and half-contextualization, a novel representation of contexts. Half-contextualization is based on the half-context hypothesis that states that the distributional characteristics of a word or bigram are best represented by treating its context distribution to the left and right separately and that only directionally relevant distributional information should be used. Clustering is achieved using a new clustering algorithm

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Fan, Ju, Zihui Gu, Songyue Zhang, et al. "Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL." Proceedings of the VLDB Endowment 17, no. 11 (2024): 2750–63. http://dx.doi.org/10.14778/3681954.3681960.

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Zero-shot natural language to SQL (NL2SQL) aims to generalize pretrained NL2SQL models to new environments ( e.g. , new databases and new linguistic phenomena) without any annotated NL2SQL samples from these environments. Existing approaches either use small language models (SLMs) like BART and T5, or prompt large language models (LLMs). However, SLMs may struggle with complex natural language reasoning, and LLMs may not precisely align schemas to identify the correct columns or tables. In this paper, we propose a ZeroNL2SQL framework, which divides NL2SQL into smaller sub-tasks and utilizes b

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31

Oh, Jiun, and Yong-Suk Choi. "Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2seq Models for Machine Translation." Applied Sciences 11, no. 18 (2021): 8737. http://dx.doi.org/10.3390/app11188737.

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This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq models with monolingual corpora for the source and target languages, then combine the encoder of the source language model and the decoder of the target language model, i.e., the cross-connection. We add an intermediate layer between the pre-trained encoder and the decoder to help the mapping of each other since the modules are pre-trained completely independently. These monolingual pre-trained models can work as a multilingual pre-trained model because one

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Dong, Li. "Learning natural language interfaces with neural models." AI Matters 7, no. 2 (2021): 14–17. http://dx.doi.org/10.1145/3478369.3478375.

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Language is the primary and most natural means of communication for humans. The learning curve of interacting with various services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages.

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ALAHYANE, Latifa Mohamed. "APPLIED LINGUISTIC APPROACH TO TEACHING FOREIGN LANGUAGES THEORETICAL AND METHODOLOGICAL MODELS." International Journal of Humanities and Educational Research 03, no. 05 (2021): 371–92. http://dx.doi.org/10.47832/2757-5403.5-3.32.

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The theoretical achievement in the field of foreign language learning in the 1950s and early ‎‎1960s remained related to the practical side of language teaching. Moreover, The idea of the ‎need for foreign language teaching methodologies for a theory of learning has remained constant ‎since the occurrence of educational reform movements of the late nineteenth century.‎ To come to terms with the current developments in the field of foreign language learning, it is ‎necessary to trace the recent history of the research carried out in this regard. Therefore, we will ‎focus in this article on trac

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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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Mozafari, Marzieh, Khouloud Mnassri, Reza Farahbakhsh, and Noel Crespi. "Offensive language detection in low resource languages: A use case of Persian language." PLOS ONE 19, no. 6 (2024): e0304166. http://dx.doi.org/10.1371/journal.pone.0304166.

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THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Different types of abusive content such as offensive language, hate speech, aggression, etc. have become prevalent in social media and many efforts have been dedicated to automatically detect this phenomenon in different resource-rich languages such as English. This is mainly due to the comparative lack of annotated data related to offensive language in low-resource languages, especially the ones spoken in Asian countries. To reduce the vulnerability among social media users from these regions

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Kalikova, Anna Mikhailovna, Maria Vladimirovna Volkova, Zulfia Kapizovna Tastemirova, Julia Evgenievna Bespalova, and Olga Borisovna Bagrintseva. "Structural differences of syntactic models in Russian and Chinese." SHS Web of Conferences 164 (2023): 00083. http://dx.doi.org/10.1051/shsconf/202316400083.

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For many centuries of scientific existence linguistic researchers and philosophic leaders have been trying to establish a rigid determination of linguistic concepts. Theories of formal generative grammar provide the opportunity for logically proved abstract models. Such models allowing to reflect the typological features of Chinese and Russian syntactical structures in the most accessible way. According to the language morphological classification, the world's languages are divided into four morphological groups: a. inflectional languages, b. agglutinative languages, c. isolating languages, d.

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Mukhamadiyev, Abdinabi, Mukhriddin Mukhiddinov, Ilyos Khujayarov, Mannon Ochilov, and Jinsoo Cho. "Development of Language Models for Continuous Uzbek Speech Recognition System." Sensors 23, no. 3 (2023): 1145. http://dx.doi.org/10.3390/s23031145.

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Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrence

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38

Murray, Robert W. "On Models of Syllable Division." Revue québécoise de linguistique 18, no. 2 (2009): 151–69. http://dx.doi.org/10.7202/602657ar.

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AbstractPicard (1983, 1987b) claims that his model of syllable division predicts the placement of a syllable boundary in any given sequence of segments for a particular language. In this article, I show that this model is inadequate in three ways; a) it does not take into consideration language specific differences in syllable structure, particularly of sequences of the typeVPLVandVPGV(whereP= plosive,L= liquid, andG= glide) which can be syllabifiedV$PLV/V$PGVorVP$LV/VP$GVdepending on language specific factors, b) it fails to predict the correct placement of syllable boundaries for certain lan

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Dorado, Rubén. "Statistical models for languaje representation." Revista Ontare 1, no. 1 (2015): 29. http://dx.doi.org/10.21158/23823399.v1.n1.2013.1208.

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ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAThis paper discuses several models for the computational representation of language. First, some n-gram models that are based on Markov models are introduced. Second, a family of models known as the exponential models is taken into account. This family in particular allows the incorporation of several features to model. Third, a recent current of research, the probabilistic Bayesian approach, is discussed. In this kind of models, language is modeled as a probabilistic distribution. Several distributions and probabilistic processes, s

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40

Kashyap, Gaurav. "Multilingual NLP: Techniques for Creating Models that Understand and Generate Multiple Languages with Minimal Resources." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem7648.

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Models that can process human language in a variety of applications have been developed as a result of the quick development of natural language processing (NLP). Scaling NLP technologies to support multiple languages with minimal resources is still a major challenge, even though many models work well in high-resource languages. By developing models that can comprehend and produce text in multiple languages, especially those with little linguistic information, multilingual natural language processing (NLP) seeks to overcome this difficulty. This study examines the methods used in multilingual

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Oralbekova, Dina, Orken Mamyrbayev, Mohamed Othman, Dinara Kassymova, and Kuralai Mukhsina. "Contemporary Approaches in Evolving Language Models." Applied Sciences 13, no. 23 (2023): 12901. http://dx.doi.org/10.3390/app132312901.

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This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into di

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Ilyas, Mohammed. "Language quotient (LQ): new models of language learning." International Journal of ADVANCED AND APPLIED SCIENCES 3, no. 9 (2016): 44–50. http://dx.doi.org/10.21833/ijaas.2016.09.008.

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Irtza, Saad, Vidhyasaharan Sethu, Eliathamby Ambikairajah, and Haizhou Li. "Using language cluster models in hierarchical language identification." Speech Communication 100 (June 2018): 30–40. http://dx.doi.org/10.1016/j.specom.2018.04.004.

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Shi, Zhouxing, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh. "Red Teaming Language Model Detectors with Language Models." Transactions of the Association for Computational Linguistics 12 (2024): 174–89. http://dx.doi.org/10.1162/tacl_a_00639.

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Abstract The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent work has proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM’s output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writ

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Lee, Chanhee, Kisu Yang, Taesun Whang, Chanjun Park, Andrew Matteson, and Heuiseok Lim. "Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models." Applied Sciences 11, no. 5 (2021): 1974. http://dx.doi.org/10.3390/app11051974.

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Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved b

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Alostad, Hana. "Large Language Models as Kuwaiti Annotators." Big Data and Cognitive Computing 9, no. 2 (2025): 33. https://doi.org/10.3390/bdcc9020033.

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Stance detection for low-resource languages, such as the Kuwaiti dialect, poses a significant challenge in natural language processing (NLP) due to the scarcity of annotated datasets and specialized tools. This study addresses these limitations by evaluating the effectiveness of open large language models (LLMs) in automating stance detection through zero-shot and few-shot prompt engineering, with a focus on the potential of open-source models to achieve performance levels comparable to those of closed-source alternatives. We also highlight the critical distinctions between zero- and few-shot

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Tantug, Ahmet Cüneyd. "Document Categorization with Modified Statistical Language Models for Agglutinative Languages." International Journal of Computational Intelligence Systems 3, no. 5 (2010): 632. http://dx.doi.org/10.2991/ijcis.2010.3.5.12.

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Ayres‐Bennett, Wendy. "Researching Language Standards and Standard Languages: Theories, Models and Methods." Transactions of the Philological Society 122, no. 3 (2024): 496–503. https://doi.org/10.1111/1467-968x.12298.

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AbstractThe title of Jim Adams's rich and interesting paper clearly states the key question at the heart of his analysis: ‘Was classical (late republican) Latin a “standard language”?’. In this article, I contextualise some of his answers to this and other related questions he raises by situating them in the context of theoretical discussions of standardisation and recent explorations of its processes and outcomes. In recent years there has been extensive research on linguistic standardisation. This research has broadened the scope of consideration from the now stock examples of modern Western

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Feng, Hui. "Different languages, different cultures, different language ideologies, different linguistic models." Journal of Multicultural Discourses 4, no. 2 (2009): 151–64. http://dx.doi.org/10.1080/17447140802283191.

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Tantug, Ahmet Cüneyd. "Document Categorization with Modified Statistical Language Models for Agglutinative Languages." International Journal of Computational Intelligence Systems 3, no. 5 (2010): 632–45. http://dx.doi.org/10.1080/18756891.2010.9727729.

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