# Towards Probabilistic Question Answering Over Tabular Data

Source: [Towards Probabilistic Question Answering Over Tabular Data](<https://www.sota2.com/research/paper/68fa79a21e209131cea7e949>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:02:45.345Z

Authors: Chen Shen, Sajjadur Rahman, Estevam Hruschka

Published: 2025-06-25T18:15:33.000Z

## Abstract

Current approaches for question answering (QA) over tabular data, such as NL2SQL systems, perform well for factual questions where answers are directly retrieved from tables. However, they fall short on probabilistic questions requiring reasoning under uncertainty. In this paper, we introduce a new benchmark LUCARIO and a framework for probabilistic QA over large tabular data. Our method induces Bayesian Networks from tables, translates natural language queries into probabilistic queries, and uses large language models (LLMs) to generate final answers. Empirical results demonstrate significant improvements over baselines, highlighting the benefits of hybrid symbolic-neural reasoning.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.20747>)

[PDF](<https://arxiv.org/pdf/2506.20747.pdf>)