Academic literature on the topic 'Tabular language'

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Journal articles on the topic "Tabular language"

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Badaro, Gilbert, and Paolo Papotti. "Transformers for tabular data representation." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3746–49. http://dx.doi.org/10.14778/3554821.3554890.

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In the last few years, the natural language processing community witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial, we present these proposals with two main goals. First, we introduce to a database audience the potentials and the limitations of current models. Second, we demonstrate the large variety of data applications that benefit from the transformer architecture. T
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Benecke, Klaus, and Andreas Hauptmann. "Does the School Need a Tabular Computer Language?" International Journal for Digital Society 2, no. 3 (2011): 525–32. http://dx.doi.org/10.20533/ijds.2040.2570.2011.0063.

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Wang, Guanchu, Yuzhong Chen, Huiyuan Chen, et al. "Advancing Table Understanding of Large Language Models via Feature Re-ordering." ACM SIGKDD Explorations Newsletter 27, no. 1 (2025): 112–23. https://doi.org/10.1145/3748239.3748248.

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Large Language Models (LLMs) exhibit exceptional proficiency in comprehending human language. Despite their significant success across a wide array of tasks, understanding tabular data remains a challenging task. Especially, tabular data lacks an intrinsic order of the different features (table fields), whereas LLMs take only sequential inputs. Consequently, an artificial order is imposed, the impact of which on the performance of LLMs has not yet been thoroughly investigated. Surprisingly, as discovered in this work, this artificially induced order bias dramatically influences the performance
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Tang, Yaling, and Peng Yang. "Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification." Journal of Physics: Conference Series 2303, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2303/1/012030.

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Abstract Tabular fact verification is a challenging task that requires obtaining relevant evidence from the table and utilizing them to verify a given claim. The main difficulty in tabular fact verification is that traditional language models cannot capture the underlying information carried in tabular data. To solve this problem, we propose GraERR, a Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification. It consists of two modules: a data initial representation module based on the DeBERTa model and a graph-augmented representation and inference module. The former imp
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Isomura, Tokimasa, Ryotaro Shimizu, and Goto Masayuki. "LLMOverTab: Tabular data augmentation with language model-driven oversampling." Expert Systems with Applications 264 (March 2025): 125852. http://dx.doi.org/10.1016/j.eswa.2024.125852.

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Bussotti, Jean-Flavien, Enzo Veltri, Donatello Santoro, and Paolo Papotti. "Generation of Training Examples for Tabular Natural Language Inference." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626730.

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Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that p
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Odiljonova, Nilufar. "USINGMEDIAEDUCATIONINTEACHINGUZBEK AS FOREIGN LANGUAGE." Uzbekistan: language and culture 2, no. 2 (2024): 76–82. http://dx.doi.org/10.52773/tsuull.aphil.2024.2.5/virt4377.

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In this article, the use of media education in teaching Uzbek as a foreignlanguageisdiscussedtobeofspecialimportancefortheformation of students’ worldview, development and fluency of their oral speech. It was also thought that playing interactive games during the lesson, using picturesonthetopic,constructingsentencesbasedonthem,analyzingsit-uations and finding answers to tabular questions will increase the effec-tiveness of the lesson and make it interesting.
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Forster, Carlos Henrique Q. "Programming through Spreadsheets and Tabular Abstractions." JUCS - Journal of Universal Computer Science 13, no. (6) (2007): 806–16. https://doi.org/10.3217/jucs-013-06-0806.

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The spreadsheet metaphor has, over the years, proved itself valuable for the definition and use of computations by non-programmers. However, the computation model adopted in commercial spreadsheets is still limited to non-recursive computations and lacks abstraction mechanisms that would provide modularization and better reuse (beyond copy and paste). We investigate these problems by identifying a minimal set of requirements for recursive computations, designing a spreadsheet-based language with an abstraction definition mechanism, prototyping an interpreter and evaluating it with examples.
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Wang, Zhensheng, Wenmian Yang, Kun Zhou, Yiquan Zhang, and Weijia Jia. "RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25452–60. https://doi.org/10.1609/aaai.v39i24.34734.

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The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction informa
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Majee, Anay, Maria Xenochristou, and Wei-Peng Chen. "TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19387–95. https://doi.org/10.1609/aaai.v39i18.34134.

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Handling heterogeneous data in tabular datasets poses a significant challenge for deep learning models. While attention-based architectures and self-supervised learning have achieved notable success, their application to tabular data remains less effective over linear and tree based models. Although several breakthroughs have been achieved by models which transform tables into uni-modal transformations like image, language and graph, these models often underperform in the presence of feature heterogeneity. To address this gap, we introduce TabGLM (Tabular Graph Language Model), a novel multi-m
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Dissertations / Theses on the topic "Tabular language"

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Lee, Chen-Hsiu. "A tabular propositional logic: and/or Table Translator." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2409.

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The goal of this project is to design a tool to help users translate any logic statement into Disjunctive Normal Form and present the result as an AND/OR TABLE, which makes the logic relation easier to express by using a two-dimensional grid of values or expressions. This tool is implemented through a web-based and Java-based application. Thus, the user can utilize this tool via World Wide Web.
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CREMASCHI, MARCO. "ENABLING TABULAR DATA UNDERSTANDING BY HUMANS AND MACHINES THROUGH SEMANTIC INTERPRETATION." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/263555.

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Esiste un numero significativo di documenti, report e pagine Web – un'analisi riporta 233 milioni di tabelle relazionali nel repository Common Crawl contenente un totale 2,85 miliardi di documenti – che fanno uso di tabelle per fornire informazioni che non possono essere facilmente elaborate dagli umani o capite dai computer. Per risolvere questo problema proponiamo un nuovo approccio che permetterà ai computer di interpretare la semantica di una tabella, e fornirà agli umani una rappresentazione più accessibile dei dati contenuti in essa. Per conseguire questo obiettivo, il problema principal
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Szymczak, Marcin. "Programming language semantics as a foundation for Bayesian inference." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28993.

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Bayesian modelling, in which our prior belief about the distribution on model parameters is updated by observed data, is a popular approach to statistical data analysis. However, writing specific inference algorithms for Bayesian models by hand is time-consuming and requires significant machine learning expertise. Probabilistic programming promises to make Bayesian modelling easier and more accessible by letting the user express a generative model as a short computer program (with random variables), leaving inference to the generic algorithm provided by the compiler of the given language. Howe
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Višnja, Ognjenović. "Aproksimativna diskretizacija tabelarno organizovanih podataka." Phd thesis, Univerzitet u Novom Sadu, Tehnički fakultet Mihajlo Pupin u Zrenjaninu, 2016. https://www.cris.uns.ac.rs/record.jsf?recordId=101259&source=NDLTD&language=en.

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Disertacija se bavi analizom uticaja raspodela podataka na rezultate algoritama diskretizacije u okviru procesa mašinskog učenja. Na osnovu izabranih baza i algoritama diskretizacije teorije grubih skupova i stabala odlučivanja, istražen je uticaj odnosa raspodela podataka i tačaka reza određene diskretizacije.Praćena je promena konzistentnosti diskretizovane tabele u zavisnosti od položaja redukovane tačke reza na histogramu. Definisane su fiksne tačke reza u zavisnosti od segmentacije multimodal raspodele, na osnovu kojih je moguće raditi redukciju preostalih tačaka reza. Za određivan
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Solodovnik, D. L., M. L. Malinovsky, and A. P. Konishcheva. "Developing of Digital Systems Based on Table Tools THDL." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47239.

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Pluháček, David. "Syntaktická analýza založená na modifikovaných zásobníkových automatech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-236851.

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The thesis introduces new models for formal languages, the m-limited state   grammar and the deep pushdown automaton. Their basic definitions are presented,   so is their mutual equivalence and the characteristics of the language family they describe.   Following, a parsing method based on these models is presented. The method is an extension   of a similar method used for context-free languages, the table driven parsing.   The final part of the thesis describes the implementation of a parser based on the method.
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Jakub, Dušan. "Zpětný překlad vysokoúrovňových konstrukcí jazyka C++." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264936.

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The thesis addresses the decompilation of high-level object-oriented C++ language from a machine code. The term reverse engineering is defined and existing decompilers are described with emphasis on their ability to reconstruct C++. AVG decompiler project is introduced, to which this thesis contributes. C++ language is analysed, both on a logical level and in the machine code and existing methods of decompilation are described. On this basis a novel method is introduced, capable of decompiling classes, their hierarchy, constructors, destructors and definitions and usages of virtual methods. Th
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Gregárek, Ondřej. "Správa vývojové dokumentace přes WWW II." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217294.

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Document server is a web application controllable by way of web browser. It is meant to serve for the management of development documentation. The application is divided to the four basic parts: Requirements, Products, Tests and Test Run. The section Requirements serves for inserting requirements for products. Product is produced on the basis of these needs and registered in part Products. Test setting is created in the part Tests according to requirements from the part Requirements. Particular products are then tested. The part Test Run registers records of these tests. These are parts of the
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Blažek, Martin. "Interaktivní interpret jazyka C." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235978.

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This master's thesis deals with implementation of ISO C99 language interpreter. The goal of this thesis is to provide support of education in C language programming and fast algorithm prototyping. It enables students to create own C programs and to experiment with language constructions without compiling. User interface includes editor and simple debugger. The interpreter is implemented in a novel grammar development environment written in Java language - ANTLRWorks which includes ANTLR language tool.
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Prost, Jean-Philippe. "Modelling Syntactic Gradience with Loose Constraint-based Parsing." Phd thesis, Université de Provence - Aix-Marseille I, 2008. http://tel.archives-ouvertes.fr/tel-00352828.

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La grammaticalité d'une phrase est habituellement conçue comme une notion binaire : une phrase est soit grammaticale, soit agrammaticale. Cependant, bon nombre de travaux se penchent de plus en plus sur l'étude de degrés d'acceptabilité intermédiaires, auxquels le terme de gradience fait parfois référence. À ce jour, la majorité de ces travaux s'est concentrée sur l'étude de l'évaluation humaine de la gradience syntaxique. Cette étude explore la possibilité de construire un modèle robuste qui s'accorde avec ces jugements humains.<br>Nous suggérons d'élargir au langage mal formé les concepts de
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Books on the topic "Tabular language"

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Silvin, Košak, Groddek Detlev, and Zorman Marina, eds. Tabularia Hethaeorum: Hethitologische Beiträge : Silvin Košak zum 65. Geburtstag. Harrassowitz, 2007.

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Vavroušek, Petr. Pán chetitských tabulek. Filozofická fakulta Univerzity Karlovy, 2009.

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Cristofani, Mauro. Tabula Capuana: Un calendario festivo di età arcaica. L.S. Olschki, 1995.

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Weiss, Michael L. Language and ritual in Sabellic Italy: The ritual complex of the third and the fourth tabulae Iguvinae. Brill, 2009.

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Cetedoc, ed. Thesaurus Iohannis Cassiani: Enumeratio formarum : index formarum a tergo ordinatarum, index formarum graecarum, index formarum secundum orthographiae normam collatarum, tabula frequentiarum, concordantia formarum. Brepols, 1992.

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Auziņas, Ilzes, and Guntas Nešpores. Latviešu valodas darbības vārdu tabulas: Macību un metodiskais materiāls = Latvian Verb Tables : study and methodological material = Tablit︠s︡a glagolov latyshskogo i︠a︡zyka : uchebno-metodicheskoe posobie. Latviešu valodas aģentūra, 2014.

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Daniela, Blech-Straub, ed. Grosse Namen, bedeutende Zitate: Von Albertus Magnus Tabula rasa bis N.P. Willis Die oberen Zehntausend : Herkunft, Bedeutung und aktueller Gebrauch. Dudenverlag, 2004.

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Paul, Chandler, and Cetedoc, eds. Thesaurus Thomae a Kempis: Enumeratio formarum,index formarum a tergo ordinatarum, index formarum teutonicarum, index formarum singulorum operum, index formarum secundum orthographiae normam collatarum, tabulae frequentiarum, concordantia formarum. Brepols, 1994.

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CETEDOC and Université catholique de Louvain (1970- ), eds. Thesaurus Marii Victorini: Series A--formae : enumeratio formarum, index formarum a tergo ordinatarum, index formarum graecarum, index formarum singulorum operum, index formarum secundum orthographiae norman collatarum, tabula frequentiarum, concordantia formarum. Brepols, 1993.

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Pierre, Beguin, and Cetedoc, eds. Thesaurus Fratris Salimbene de Adam: Enumeratio lemmatum et formarum onomasticon, index formarum et lemmatum, index lemmatum a tergo ordinatorum, index formarum a tergo ordinatarum, tabula frequentiarum, concordantia lemmatum et formarum. Brepols, 1992.

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Book chapters on the topic "Tabular language"

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de la Clergerie, Éric Villemonte. "Natural Language Tabular Parsing." In Logic Programming. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45635-x_6.

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Zhao, Zilong, Robert Birke, and Lydia Y. Chen. "TabuLa: Harnessing Language Models for Tabular Data Synthesis." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-8186-0_20.

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Wüthrich, Mario V., and Michael Merz. "Natural Language Processing." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_10.

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AbstractThis chapter discusses natural language processing (NLP) which deals with regression modeling of non-tabular or unstructured text data. We explain how words can be embedded into low-dimension spaces that serve as numerical word encodings. These can then be used for text recognition, either using RN networks or attention layers. We give an example where we aim at predicting claim perils from claim descriptions.
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Huang, Mao, Yihai Chen, Ridha Khedri, and Huaikou Miao. "The Interchange Format of Tabular Expressions Using XML." In Structured Object-Oriented Formal Language and Method. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31220-0_3.

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Ronval, Benoît, Pierre Dupont, and Siegfried Nijssen. "Detection of Large Language Model Contamination with Tabular Data." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91398-3_18.

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Wu, Zhenyu, Jiaoyan Chen, and Norman W. Paton. "Taxonomy Inference for Tabular Data Using Large Language Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94575-5_22.

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Zhang, Tengxun, Hongfei Xu, Josef van Genabith, Deyi Xiong, and Hongying Zan. "NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering." In Natural Language Processing and Chinese Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44693-1_46.

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Yan, Mengyi, Weilong Ren, Yaoshu Wang, and Jianxin Li. "A Retrieval-Augmented Framework for Tabular Interpretation with Large Language Model." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-5779-4_23.

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Tayebi, Aida, and Ozlem Ozmen Garibay. "Evaluating Fairness and Bias in Large Language Models for Tabular Data." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93415-5_7.

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Nederhof, Mark-Jan, and Giorgio Satta. "Tabular Parsing." In Formal Languages and Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39886-8_28.

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Conference papers on the topic "Tabular language"

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Nguyen, Dang, Sunil Gupta, Kien Do, Thin Nguyen, and Svetha Venkatesh. "Generating Realistic Tabular Data with Large Language Models." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00040.

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Oh, Minsik, and Jonghoon Chun. "Query Answering for Tabular Data Using Large Language Models." In 2024 International Conference on AI x Data and Knowledge Engineering (AIxDKE). IEEE, 2024. https://doi.org/10.1109/aixdke63520.2024.00037.

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Rabbani, Shourav B., Ibna Kowsar, and Manar D. Samad. "Transfer Learning of Tabular Data by Finetuning Large Language Models." In 2024 13th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2024. https://doi.org/10.1109/icece64886.2024.11024938.

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Shankarampeta, Abhilash, Vivek Gupta, and Shuo Zhang. "Enhancing Tabular Reasoning with Pattern Exploiting Training." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-main.54.

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Manabe, Kosuke, Yukihisa Fujita, Masahiro Kuwahara, and Teruaki Hayashi. "Metadata-less Dataset Recommendation Leveraging Dataset Embeddings by Pre-trained Tabular Language Models." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825245.

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Theuma, Adrian, and Ehsan Shareghi. "Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance." In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.eacl-short.10.

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Yang, Hao, Zhaoyong Yang, Ruyang Zhao, Xiaoran Li, and Gaoqi Rao. "The implementation solution for automatic visualization of tabular data in relational databases based on large language models." In 2024 International Conference on Asian Language Processing (IALP). IEEE, 2024. http://dx.doi.org/10.1109/ialp63756.2024.10661162.

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Yang, Shuo, Chenchen Yuan, Yao Rong, Felix Steinbauer, and Gjergji Kasneci. "P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via 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.16.

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Shi, Wenqi, Ran Xu, Yuchen Zhuang, et al. "EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1245.

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Nararatwong, Rungsiman, Natthawut Kertkeidkachorn, and Ryutaro Ichise. "Enhancing Financial Table and Text Question Answering with Tabular Graph and Numerical Reasoning." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-main.72.

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