Factual Inconsistencies in Multilingual Wikipedia Tables

TL;DR

This paper investigates factual inconsistencies in multilingual Wikipedia tables, developing a methodology to analyze cross-lingual alignment and its implications for AI reliability.

Contribution

It introduces a new methodology for collecting, aligning, and categorizing factual inconsistencies in Wikipedia's multilingual tabular data.

Findings

Identified categories of factual inconsistencies across languages.

Quantified the extent of misalignments in multilingual tables.

Provided insights for improving factual verification in multilingual content.

Abstract

Wikipedia serves as a globally accessible knowledge source with content in over 300 languages. Despite covering the same topics, the different versions of Wikipedia are written and updated independently. This leads to factual inconsistencies that can impact the neutrality and reliability of the encyclopedia and AI systems, which often rely on Wikipedia as a main training source. This study investigates cross-lingual inconsistencies in Wikipedia's structured content, with a focus on tabular data. We developed a methodology to collect, align, and analyze tables from Wikipedia multilingual articles, defining categories of inconsistency. We apply various quantitative and qualitative metrics to assess multilingual alignment using a sample dataset. These insights have implications for factual verification, multilingual knowledge interaction, and design for reliable AI systems leveraging Wikipedia content.