# Leveraging LLM For Synchronizing Information Across Multilingual Tables

arXiv: https://arxiv.org/abs/2504.02559 · HTML: https://mlsanity.com/paper/2504.02559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality](https://mlsanity.com/paper/2310.07234/) (2023)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [FRUIT: Faithfully Reflecting Updated Information in Text](https://mlsanity.com/paper/2112.08634/) (2021)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [Prompting open-source and commercial language models for grammatical error correction of English learner text](https://mlsanity.com/paper/2401.07702/) (2024)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning](https://mlsanity.com/paper/2311.09821/) (2023)
- [Novel Entity Discovery from Web Tables](https://mlsanity.com/paper/2002.00206/) (2020)
- [Generating Categories for Sets of Entities](https://mlsanity.com/paper/2008.08428/) (2020)
- [InfoSync: Information Synchronization across Multilingual Semi-structured Tables](https://mlsanity.com/paper/2307.03313/) (2023)
- [SUMIE: A Synthetic Benchmark for Incremental Entity Summarization](https://mlsanity.com/paper/2406.05079/) (2024)

## Built on by (most cited first, 2 of 2)

- [Factual Inconsistencies in Multilingual Wikipedia Tables](https://mlsanity.com/paper/2507.18406/) (2025)
- [MapVerse: A Benchmark for Geospatial Question Answering on Diverse Real-World Maps](https://mlsanity.com/paper/2602.10518/) (2026)

## Abstract (arXiv)

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.