Leveraging LLM For Synchronizing Information Across Multilingual Tables

• URL: http://arxiv.org/abs/2504.02559v2
• Date: Fri, 04 Apr 2025 19:18:32 GMT
• Title: Leveraging LLM For Synchronizing Information Across Multilingual Tables
• Authors: Siddharth Khincha, Tushar Kataria, Ankita Anand, Dan Roth, Vivek Gupta,
• Abstract summary: This paper explores large language models (LLMs) for multilingual information synchronization.<n>We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables.<n>Our findings reveal that single-prompt approaches often produce suboptimal results, prompting us to introduce a task decomposition strategy.
• Score: 45.821452282988794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete.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 We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance.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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