Leveraging LLM For Synchronizing Information Across Multilingual Tables

Siddharth Khincha; Tushar Kataria; Ankita Anand; Dan Roth; Vivek Gupta

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arXiv · 2504.02559

Leveraging LLM For Synchronizing Information Across Multilingual Tables

Siddharth Khincha·Tushar Kataria·Ankita Anand·Dan Roth·Vivek Gupta

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Abstract

The vast amount of online information today poses challenges for non-English speakers, as much of it is concentrated in high-resource languages such as English and French. Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete. Recent research has sought to improve cross-language synchronization of Wikipedia tables using rule-based methods. These approaches can be effective, but they struggle with complexity and generalization. This paper explores large language models (LLMs) for multilingual information synchronization, using zero-shot prompting as a scalable solution. We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance. Our findings reveal that single-prompt approaches often produce suboptimal results, prompting us to introduce a task decomposition strategy that enhances coherence and accuracy. Our proposed method outperforms existing baselines, particularly in Information Updation (1.79%) and Information Addition (20.58%), highlighting the model strength in dynamically updating and enriching data across architectures.

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Siddharth Khincha, Tushar Kataria, Ankita Anand, Dan Roth, Vivek Gupta. 2025-04-04. Leveraging LLM For Synchronizing Information Across Multilingual Tables. https://arxiv.org/abs/2504.02559

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