trasgoDP is an open-source Python framework for releasing tabular and location data under local differential privacy and geo-indistinguishability guarantees.

The paper introduces Open-Domain Tabular Question Answering for Future Data Forecasting and Reasoning, a new dataset and framework that enables LLMs to perform time-series forecasting and reasoning on…

TabChange proposes a novel framework to generate natural and minimally altered counterfactual instances in tabular data by precisely controlling attribute modifications based on their relationship str…

This paper proposes a taxonomy-guided evaluation protocol for temporal fidelity in synthetic sequential tabular data, measuring timestamp validity, cross-sectional structure, within-entity dynamics, a…

This paper explores the use of Large Language Models (LLMs) in data fusion tasks for tabular data and shows their superiority over traditional methods.

This paper introduces TabPack, an efficient MLP ensemble for tabular data that samples and trains MLPs with different hyperparameters in parallel and selects ensemble members on-the-fly during trainin…

The paper introduces TabPrep, a feature engineering pipeline that systematically improves performance across various tabular machine learning models by addressing structural data patterns ignored by c…

This paper systematically evaluates tabular data referencing errors in large language models and presents methods to improve answer accuracy and detect errors.

The paper introduces DataGovBench, a benchmark for evaluating Large Language Models in real-world data analysis scenarios, revealing significant performance gaps with state-of-the-art models.

The paper introduces Semantic Triplet Restoration (STR), a novel protocol that converts complex table structures into atomic semantic triplets, improving table question answering by providing explicit…

This paper introduces a new benchmark dataset and evaluation framework for 'data snapshot extraction,' focusing on identifying and localizing semantically meaningful analytical artifacts within operat…

The paper introduces Geometry-Aware Tabular Diffusion (GATD), a method that enhances tabular data synthesis by explicitly incorporating pairwise geometric relationships (angles and lengths) into the d…

The paper builds a benchmark to evaluate the ability of multimodal large language models to extract accurate data tables from chart images, and proposes a human-centered approach to improve numerical…

While restricting a model to the theoretical Markov boundary can significantly improve prediction, the practical process of discovering and using this boundary is often computationally infeasible and…

This paper presents Vivace, a serverless system for exact temporal OLAP over interval histories, which addresses the issues of incomplete data and incorrect answers in serverless functions.

This paper proposes four guidelines and two novel data ordering methods (STR and SAW) to systematically optimize data organization, significantly enhancing the stability and performance of LLM trainin…

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper explains the 'table-chart gap' in scientific claim verification by showing that multimodal LLMs successfully encode information from charts but fail to route it to the final prediction layer…

This paper explores the integration of several extensions for Natural Language to SQL (NL2SQL) translation, including NatSQL representation, preprocessing, fine-tuning, and reranker model.