@inproceedings{khincha-etal-2025-leveraging,
title = "Leveraging {LLM} For Synchronizing Information Across Multilingual Tables",
author = "Khincha, Siddharth and
Kataria, Tushar and
Anand, Ankita and
Roth, Dan and
Gupta, Vivek",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.329/",
doi = "10.18653/v1/2025.naacl-long.329",
pages = "6474--6492",
ISBN = "979-8-89176-189-6",
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 Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete.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 We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance.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{'}s strength in dynamically updating and enriching data across architectures."
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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 Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete.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’s strength in dynamically updating and enriching data across architectures.</abstract>
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%0 Conference Proceedings
%T Leveraging LLM For Synchronizing Information Across Multilingual Tables
%A Khincha, Siddharth
%A Kataria, Tushar
%A Anand, Ankita
%A Roth, Dan
%A Gupta, Vivek
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F khincha-etal-2025-leveraging
%X 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’s strength in dynamically updating and enriching data across architectures.
%R 10.18653/v1/2025.naacl-long.329
%U https://aclanthology.org/2025.naacl-long.329/
%U https://doi.org/10.18653/v1/2025.naacl-long.329
%P 6474-6492
Markdown (Informal)
[Leveraging LLM For Synchronizing Information Across Multilingual Tables](https://aclanthology.org/2025.naacl-long.329/) (Khincha et al., NAACL 2025)
ACL
- Siddharth Khincha, Tushar Kataria, Ankita Anand, Dan Roth, and Vivek Gupta. 2025. Leveraging LLM For Synchronizing Information Across Multilingual Tables. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6474–6492, Albuquerque, New Mexico. Association for Computational Linguistics.