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Journal articles on the topic 'Tabular language'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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Aly, Rami, and Andreas Vlachos. "TabVer: Tabular Fact Verification with Natural Logic." Transactions of the Association for Computational Linguistics 12 (2024): 1648–71. https://doi.org/10.1162/tacl_a_00722.

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Abstract Fact verification on tabular evidence incentivizes the use of symbolic reasoning models where a logical form is constructed (e.g., a LISP-style program), providing greater verifiability than fully neural approaches. However, these logical forms typically rely on well-formed tables, restricting their use in many scenarios. An emerging symbolic reasoning paradigm for textual evidence focuses on natural logic inference, which constructs proofs by modeling set-theoretic relations between a claim and its evidence in natural language. This approach provides flexibility and transparency but

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Tekin, Ender, Qian You, Devin M. Conathan, Glenn M. Fung, and Thomas S. Kneubuehl. "Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12414–22. http://dx.doi.org/10.1609/aaai.v36i11.21507.

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We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. Th

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An, Seunghwan, Gyeongdong Woo, Jaesung Lim, ChangHyun Kim, Sungchul Hong, and Jong-June Jeon. "Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15356–64. https://doi.org/10.1609/aaai.v39i15.33685.

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In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu). Since the MLu performance depends on accurately approximating the conditional distributions, we focus on devising a synthetic data generation method based on conditional distribution estimation. We introduce MaCoDE by redefining the consecutive multi-class classification task of Masked Language Modeling (MLM) as histogram-based non-parametric conditional density estimation. Our approach enables the estimation of conditional densities across arbitrary c

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Babu, R. Anand, Vishwa Priya V, Manoj Kumar Mishra, Inakoti Ramesh Raja, Surya Kiran Chebrolu, and B. Swarna. "Transformer-Based Tabular Foundation Models: Outperforming Traditional Methods with TabPFN." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 448–55. https://doi.org/10.52088/ijesty.v5i3.1146.

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Scientific research and commercial applications rely heavily on tabular data, yet efficiently modelling this data has constantly been a problem. For over twenty years, the standard method for machine learning has been based on traditional models, with gradient-boosted decision trees (GBDTs). Despite recent advancements in deep learning, neural networks often fail to provide satisfactory results on compact tabular datasets due to factors such as overfitting, insufficient data intricate feature relationships. The study offers a Tabular Prior data Fitted Network, a foundation model developed by m

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Reddy, Sesha, Kharidhi Laxman Vandana, and Shishir Ram Shetty. "Oral and periodontal features of autosomal recessive syndromes: a tabular review." Brazilian Dental Science 22, no. 2 (2019): 155–62. http://dx.doi.org/10.14295/bds.2019.v22i2.1713.

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Objective: To systematically review the data and results of case reports of autosomal recessive syndromes associated with periodontitis. Material and Methods: An internet search using Google and PubMed search engine and keywords- autosomal recessive, periodontitis, syndromes, periodontium and gingiva was carried out. Full-text articles in the English language of all the case reports and reviews that were published in journals from the year 1966 to 2016 were obtained and evaluated and presented in tabular form. Abstracts and articles published in other languages were not included in the review.

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Wu, Yang, Yao Wan, Hongyu Zhang, et al. "Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–28. http://dx.doi.org/10.1145/3654992.

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The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their pot

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Khedkar, Sanskruti, Shilpa Lambor, Yogita Narule, and Prathamesh Berad. "Categorical Embeddings for Tabular Data using PyTorch." ITM Web of Conferences 56 (2023): 02002. http://dx.doi.org/10.1051/itmconf/20235602002.

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Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors in lowdimensional space. PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Ou

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Badaro, Gilbert, Mohammed Saeed, and Paolo Papotti. "Transformers for Tabular Data Representation: A Survey of Models and Applications." Transactions of the Association for Computational Linguistics 11 (2023): 227–49. http://dx.doi.org/10.1162/tacl_a_00544.

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Abstract In the last few years, the natural language processing community has witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in tabular data, recent research efforts extend LMs by developing neural representations for structured data. In this article, we present a survey that analyzes these efforts. We first abstract the different systems according to a traditional machine learning pipeline in terms of training data, input representation, model training, and supported downstream tasks. For eac

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Wittenburg, Kent, Louis Weitzman, and Jim Talley. "Unification-based grammars and tabular parsing for graphical languages." Journal of Visual Languages & Computing 2, no. 4 (1991): 347–70. http://dx.doi.org/10.1016/s1045-926x(05)80004-7.

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Uy, Wayne Isaac T., Galileo Arturo Gonzalez Conchas, Jiang Chen He, et al. "Abstract B012: Prompting Large Language Models to Predict Adverse Events during Cancer Treatment." Clinical Cancer Research 31, no. 13_Supplement (2025): B012. https://doi.org/10.1158/1557-3265.aimachine-b012.

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Abstract Background: Large language models (LLMs) excel on standardized oncology exams; however, their broader clinical utility remains unclear. LLMs are easy to use through “prompting.” For example, a doctor or patient can provide a clinical note and ask about the probability of an adverse event (AE). Current AE prediction relies on machine learning using tabular data, which requires substantial engineering to adapt to specific tasks and settings, making them costly and less generalizable. We compared prompting LLMs against tabular ML models to predict AEs during systemic cancer therapy. Mate

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Zhu, Jun-Peng, Peng Cai, Kai Xu, et al. "AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models." Proceedings of the VLDB Endowment 17, no. 12 (2024): 3920–33. http://dx.doi.org/10.14778/3685800.3685816.

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With the growing significance of data analysis, several studies aim to provide precise answers to users' natural language questions from tables, a task referred to as tabular question answering (TQA). The state-of-the-art TQA approaches are limited to handling only single-table questions. However, real-world TQA problems are inherently complex and frequently involve multiple tables, which poses challenges in directly extending single-table TQA designs to handle multiple tables, primarily due to the limited extensibility of the majority of single-table TQA methods. This paper proposes AutoTQA,

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Wu, Jing, Suiyao Chen, Qi Zhao, et al. "SwitchTab: Switched Autoencoders Are Effective Tabular Learners." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15924–33. http://dx.doi.org/10.1609/aaai.v38i14.29523.

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Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing (NLP), where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular data is challenging due to the less pronounced dependencies among data samples. In this paper, we address this limitation by introducing SwitchTab, a novel self-supervised method specifically designed to capture latent dependencies in tabular data. SwitchTab leverages an asymmetric encoder-decoder framework to decouple mutual and salient feature

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Ruslida, Vivi Melaty, Barnabas Sembiring, and Indah Damayanti. "FIGURATIVE LANGUAGE IN WILLIAM SHAKESPEARE AND WILLIAM WORDWORTH’S POEM." Edu-Ling: Journal of English Education and Linguistics 2, no. 2 (2019): 145. http://dx.doi.org/10.32663/edu-ling.v2i2.1098.

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This study aims to determine the use and meaning of figurative language (majas) in poetry. The sample of this study is ten poems from two different writers of the era, namely Shakespeare and Wordsworth. This research uses a descriptive qualitative method and an objective approach used to analyze data. Figurative language (majas) was analyzed based on theories from Wren and Martin and also analyzed the meaning of each figurative language (majas) that had been discovered. The figurative language (majas) found is presented in tabular form. From the data analysis, all figurative languages ??(majas

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Li, Diya, and Zhe Zhang. "MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligence." PLOS ONE 18, no. 11 (2023): e0293034. http://dx.doi.org/10.1371/journal.pone.0293034.

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Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data fr

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Zhao, Xiaoyong, Xingxin Leng, Lei Wang, and Ningning Wang. "Research on Fine-Tuning Optimization Strategies for Large Language Models in Tabular Data Processing." Biomimetics 9, no. 11 (2024): 708. http://dx.doi.org/10.3390/biomimetics9110708.

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Recent advancements in natural language processing (NLP) have been significantly driven by the development of large language models (LLMs). Despite their impressive performance across various language tasks, these models still encounter challenges when processing tabular data. This study investigates the optimization of fine-tuning strategies for LLMs specifically in the context of tabular data processing. The focus is on the effects of decimal truncation, multi-dataset mixing, and the ordering of JSON key–value pairs on model performance. Experimental results indicate that decimal truncation

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Dargahi Nobari, Arash, and Davood Rafiei. "DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–24. http://dx.doi.org/10.1145/3639279.

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Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are re

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Wang, Yuxiang, Jianzhong Qi, and Junhao Gan. "Accurate and Regret-Aware Numerical Problem Solver for Tabular Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12775–83. https://doi.org/10.1609/aaai.v39i12.33393.

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Question answering on free-form tables (a.k.a. TableQA) is a challenging task because of the flexible structure and complex schema of tables. Recent studies use Large Language Models (LLMs) for this task, exploiting their capability in understanding the questions and tabular data, which are typically given in natural language and contain many textual fields, respectively. While this approach has shown promising results, it overlooks the challenges brought by numerical values which are common in tabular data, and LLMs are known to struggle with such values. We aim to address this issue, and we

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Wu, Xianjie, Jian Yang, Linzheng Chai, et al. "TableBench: A Comprehensive and Complex Benchmark for Table Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25497–506. https://doi.org/10.1609/aaai.v39i24.34739.

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Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities. Despite these achievements, LLMs still encounter significant challenges when applied in industrial scenarios, particularly due to the increased complexity of reasoning required with real-world tabular data, underscoring a notable disparity between academic benchmarks and practical applications. To address this discrepancy, we conduct a detailed investigation into the application of tabular data in industrial scenarios an

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Akella, Ashlesha, and Krishnasuri Narayanam. "Data Wrangling Task Automation Using Code-Generating Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29616–18. https://doi.org/10.1609/aaai.v39i28.35344.

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Ensuring data quality in large tabular datasets is a critical challenge, typically addressed through data wrangling tasks. Traditional statistical methods, though efficient, cannot often understand the semantic context and deep learning approaches are resource-intensive, requiring task and dataset-specific training. We present an automated system that utilizes large language models to generate executable code for tasks like missing value imputation, error detection, and error correction. Our system aims to identify inherent patterns in the data while leveraging external knowledge, effectively

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He, Xinyi, Mengyu Zhou, Xinrun Xu, et al. "Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18206–15. http://dx.doi.org/10.1609/aaai.v38i16.29779.

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Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity.

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Bakr, Muhammad Abu, Ahmad Jaffar Khan, Sultan Daud Khan, Mohammad Haseeb Zafar, Mohib Ullah, and Habib Ullah. "Evaluation of Learning-Based Models for Crop Recommendation in Smart Agriculture." Information 16, no. 8 (2025): 632. https://doi.org/10.3390/info16080632.

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The use of intelligent crop recommendation systems has become crucial in the era of smart agriculture to increase yield and enhance resource utilization. In this study, we compared different machine learning (ML), and deep learning (DL) models utilizing structured tabular data for crop recommendation. During our experimentation, both ML and DL models achieved decent performance. However, their architectures are not suited for setting up conversational systems. To overcome this limitation, we converted the structured tabular data to descriptive textual data and utilized it to fine-tune Large La

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Yan, Liuxi, and Yaoqun Xu. "XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data." Applied Sciences 14, no. 13 (2024): 5826. http://dx.doi.org/10.3390/app14135826.

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Graph neural networks (GNNs) perform well in text analysis tasks. Their unique structure allows them to capture complex patterns and dependencies in text, making them ideal for processing natural language tasks. At the same time, XGBoost (version 1.6.2.) outperforms other machine learning methods on heterogeneous tabular data. However, traditional graph neural networks mainly study isomorphic and sparse data features. Therefore, when dealing with tabular data, traditional graph neural networks encounter challenges such as data structure mismatch, feature selection, and processing difficulties.

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Phan Thi Hoai, Nguyen Minh Phuc, Nguyen Huu Hieu, Dr Thuy Pham Thanh, and Le Thi Lan. "Tabular text embedding for Vietnamese text-based person search." Journal of Military Science and Technology 93, no. 93 (2024): 128–36. http://dx.doi.org/10.54939/1859-1043.j.mst.93.2024.128-136.

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Vietnamese text-based person search is still a challenging problem with the limited dataset of Vietnamese descriptions. The current popular approach to this problem is Deep Neural Networks (DNNs), and recently, transformer networks have been more favored because of their outperformance over CNN and RNN networks for both vision and natural language processing tasks. However, DNN, or transformer networks, require a large amount of training data and computing time for efficient learning of visual and textual features. This brings a burden for implementing Vietnamese text-based person search by DN

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Miyata, Takashi, and Yuji Matsumoto. "Natural Language Generation for Legal Expert System and Visualization of Generation Process." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 1 (1998): 26–33. http://dx.doi.org/10.20965/jaciii.1998.p0026.

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An HPSG-based grammar and a sentence generation system for a small set of Japanese in legal expert domains are constructed. The system adopts its own general semantic system in which a domain-specific logical form is converted. This separation between domain-specific and linguistic semantics gives flexibility to both task processing and sentence generation. We also propose a visualization system which shows the generation process in a tabular form and operates as a graphical user interface for grammar debugging.

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Hussein, Abdullah Uday, and Rafida Mansour. "Intonation Analysis of The Movie." Al-Adab Journal 3, no. 143 (2022): 17–26. http://dx.doi.org/10.31973/aj.v3i143.3935.

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Language has really changed in many aspects through the past hundred years or so. There are many factors due to which this transformation is seen in the present-day life in comparison with the more distant life; the modernization of the lives of the individuals as well as the communities around the globe, the invention of advanced technology in all fields of science, the awakening scientific discoveries and the rapidity of life has brought upon some coarse changes in languages, especially in English language. The purpose of this research is to know whether the language has really changed in te

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Engombangi, Jonathan Opfointshi, Rostin Mabela Makengo, Grace Nkwese Mazoni, et al. "New Approach: Tabular Fuzzy Arithmetic of the LR Type by Jomatopfe." Asian Research Journal of Mathematics 21, no. 1 (2025): 87–97. https://doi.org/10.9734/arjom/2025/v21i1885.

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This paper presents a fuzzy approach of LR types, also called LR-type tabular fuzzy arithmetic capable of handling or computing simultaneously the kernels and supports of trapezoidal fuzzy numbers, instead of doing it separately (Advantage of this approach) in order to minimize the tedious steps of alpha-cut based approaches. On the theoretical level, the aim of this article is to explain in a concise and clear manner some basic concepts of fuzzy logic that seem to continue to complicate authors and readers (researchers) in this field, given the important place of this theory in Artificial Int

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Gupta, Vivek, Riyaz A. Bhat, Atreya Ghosal, Manish Shrivastava, Maneesh Singh, and Vivek Srikumar. "Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning." Transactions of the Association for Computational Linguistics 10 (2022): 659–79. http://dx.doi.org/10.1162/tacl_a_00482.

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Abstract Neural models command state-of-the-art performance across NLP tasks, including ones involving “reasoning”. Models claiming to reason about the evidence presented to them should attend to the correct parts of the input while avoiding spurious patterns therein, be self-consistent in their predictions across inputs, and be immune to biases derived from their pre-training in a nuanced, context- sensitive fashion. Do the prevalent *BERT- family of models do so? In this paper, we study this question using the problem of reasoning on tabular data. Tabular inputs are especially well-suited fo

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Balaka, Muhammad Imam Luthfi, David Alexander, Qiming Wang, Yue Gong, Adila Krisnadhi, and Raul Castro Fernandez. "Pneuma : Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725337.

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Finding relevant tables among databases, lakes, and repositories is the first step in extracting value from data. Such a task remains difficult because assessing whether a table is relevant to a problem does not always depend only on its content but also on the context, which is usually tribal knowledge known to the individual or team. While tools like data catalogs and academic data discovery systems target this problem, they rely on keyword search or more complex interfaces, limiting non-technical users' ability to find relevant data. The advent of large language models (LLMs) offers a uniqu

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Warni, Warni, Rengki Afria, Julisah Izar, and Maya Sari Harahap. "The Stages and Development of First Language Acquisition on Children 1,6 Years Old." Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini 7, no. 2 (2023): 2080–93. http://dx.doi.org/10.31004/obsesi.v7i2.3310.

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This study describes the stages and development of children's first language using Indonesian. The object of this research is 2 children aged 1.6 years. The purpose of this study was to describe the stages and development of the first language in children. The method used is a qualitative method with a descriptive approach and the techniques used are observation and interviews. The data collected is documentation presented in tabular form. Data analysis using transcription, identification, and classification methods. The finding of this study is that CS is faster in language acquisition develo

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Lu, Yu Jun, and Guang Wei Liu. "Research on Variant Design of Parts Based on Tabular Layouts of Article Characteristics." Advanced Materials Research 181-182 (January 2011): 782–86. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.782.

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Variant design of parts for mass customization is a rapidly custom process with standardized and modularized modules. Tabular layouts of article characteristics (TLAC) describe standardization and regularization information of product, and can expediently implement product variant design and variant response of process. On the basis of analyzing TLAC, combined with programming language Visual C++, an approach of part’s rapid responding design based on UG NX6.0 and TLAC was presented. The basic principle and the modeling method of master model technology based on TLAC were discussed. Finally, a

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Gupta, Vivek. "Advancements in AI for Reasoning with Complex Data." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28711. https://doi.org/10.1609/aaai.v39i27.35106.

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Artificial intelligence has made remarkable progress in reasoning over complex, structured, multimodal, and multilingual data, addressing critical challenges in domains such as finance and healthcare. This abstract underscores key advancements in tabular reasoning, temporal analysis, and structured multimodal reasoning. Key contributions include the development of TempTabQA, a benchmark for temporal question answering, along with novel methods for enhancing temporal reasoning in large language models (LLMs). Additionally, a framework for evaluating mathematical reasoning in financial documents

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Tripathi, Shankar Sharan. "Intelligent Document Processing for Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1874–81. http://dx.doi.org/10.22214/ijraset.2024.61951.

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Abstract: This project endeavors to develop an intelligent document processing pipeline tailored specifically for medical reports, with a primary focus on samples from Dr Lal Path lab and targeting prevalent diseases leading to kidney failures such as glomerulonephritis, chronic kidney disease, polycystic kidney disease, hypertensive nephropathy, and lupus nephritis. The proposed pipeline integrates cutting-edge technologies including the YOLOv8 object detection model for precise cropping of tabular data, Paddle OCR for accurate extraction of information from tabular images, and Fuzzy Wuzzy NL

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Kang, Xiaoqiang, Zimu Wang, Xiaobo Jin, Wei Wang, Kaizhu Huang, and Qiufeng Wang. "Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24303–11. https://doi.org/10.1609/aaai.v39i23.34607.

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Solving tabular math word problems (TMWPs) has become a critical role in evaluating the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP samples are commonly required for fine-tuning. Since the collection of high-quality TMWP datasets is costly and time-consuming, recent research has concentrated on automatic TMWP generation. However, current generated samples usually suffer from issues of either correctness or diversity. In this paper, we propose a Template-driven LLM-paraphrased (TeLL) framework for generating high-quality TMWP samples with diverse backg

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Idrisov, H. V., R. A. Ibragimov, and H. V. Idrisov. "On the need for a specialized Russian-Chechen-Arabic dictionary of terms and legal categories." Minbar. Islamic Studies 16, no. 1 (2023): 152–74. http://dx.doi.org/10.31162/2618-9569-2023-16-1-152-174.

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The scientific article is devoted to the study of some legal categories, terms that are used by Russian-speaking researchers in the process of studying the Islamic system of law, Fiqh, Sharia, the religion of Islam in general. As a justification for the relevance of the work, it is indicated that in Russian Academic Islamic studies there is a need for standardization and typification of a conceptual apparatus for the objective understanding and interpretation of various sources of the Islam religion and law, especially when it comes to issues of responsibility, analysis of the actions of the s

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Mobley, Dave, Adrienne Corwin, and Brent Harrison. "Using Natural Language to Improve Hierarchical Reinforcement Learning in Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 20, no. 1 (2024): 208–16. http://dx.doi.org/10.1609/aiide.v20i1.31881.

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This work investigates how natural language task descriptions can accelerate reinforcement learning in games. Recognizing that human descriptions often imply a hierarchical task structure, we propose a method to extract this hierarchy and convert it into "options" – policies for solving subtasks. These options are generated by grounding natural language descriptions into environment states, which are then used as task boundaries to learn option policies either by leveraging prior successful traces or from human created walkthroughs. We evaluate our approach in both a simpler grid-world environ

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Papotti, Paolo. "Technical Perspective of TURL." ACM SIGMOD Record 51, no. 1 (2022): 32. http://dx.doi.org/10.1145/3542700.3542708.

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Several efforts aim at representing tabular data with neural models for supporting target applications at the intersection of natural language processing (NLP) and databases (DB) [1-3]. The goal is to extend to structured data the recent neural architectures, which achieve state of the art results in NLP applications. Language models (LMs) are usually pre-trained with unsupervised tasks on a large text corpus. The output LM is then fine-tuned on a variety of downstream tasks with a small set of specific examples. This process has many advantages, because the LM contains information about textu

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Abasilov, Aman, and Bijomart Kapalbek. "Linguistic dynamics and language policy in Kazakhstan." European Journal of Language Policy 16, no. 2 (2024): 155–76. http://dx.doi.org/10.3828/ejlp.2024.9.

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The research relevance of language policy methods, as well as the current language processes in society, is predefined by an urgent need to form nation-states with their own language and culture. The research aims to create an idea of the language changes in Kazakhstan during the period of independence and assess the language policy and its prospects at the present stage. The research employed the following methods: comparative-comparative, statistical (basic), analytical-synthetic, and graphic (auxiliary). This research examined the main parameters of language modernisation in Kazakhstan in r

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Berenguer, Alberto, Adriana Morejón, David Tomás, and Jose-Norberto Mazón. "Leveraging Large Language Models for Sensor Data Retrieval." Applied Sciences 14, no. 6 (2024): 2506. http://dx.doi.org/10.3390/app14062506.

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The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it

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Cornacchia, Giandomenico, Giulio Zizzo, Kieran Fraser, Muhammad Zaid Hameed, Ambrish Rawat, and Mark Purcell. "MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 304–15. http://dx.doi.org/10.1609/aies.v7i1.31638.

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The proliferation of Large Language Models (LLMs) in diverse applications underscores the pressing need for robust security measures to thwart potential jailbreak attacks. These attacks exploit vulnerabilities within LLMs, endanger data integrity and user privacy. Guardrails serve as crucial protective mechanisms against such threats, but existing models often fall short in terms of both detection accuracy, and computational efficiency. This paper advocates for the significance of jailbreak attack prevention on LLMs, and emphasises the role of input guardrails in safeguarding these models. We

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Unubi, Abraham Sunday. "A Contrastive Study of English and Igala Segmental Phonemes: Implications for ESL Teachers and Learners." Journal of Biomedical Engineering and Medical Imaging 6, no. 6 (2019): 31–43. http://dx.doi.org/10.14738/jbemi.66.8012.

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This paper investigated a contrastive study of English and Igala segmental phonemes: implications for English as a Foreign Language (EFL) teachers and learners. A contrastive analysis is a linguistic tool used in comparing two unrelated languages, and the main objective of it is to bring out the differences in the two languages compared with a view to emphasising on the effects which such differences have on both EFL teachers and learners. This research appealed only to the secondary sources of data, which included the orthographies of both languages under study. The Igala orthography was obta

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