Leveraging LLM For Synchronizing Information Across Multilingual Tables (2025)

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 Wikipedia reflects this imbalance, with content in low-resource languages frequently outdated or incomplete.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 We introduce the Information Updation dataset, simulating the real-world process of updating outdated Wikipedia tables, and evaluate LLM performance.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.

## introduction in today's digital era, nearly every subject/domain is discoverable online. with global access to high-speed internet expanding, the volume of information grows exponentially 12 . from movies and celebrities to elections and corporate news, a vast array of topics is just a click away for those with access. however, since developed countries-particularly english-speaking ones-were early adopters of the internet, much online content is tailored to english-speaking audiences 3 . this is evident on platforms such as wikipedia and youtube, where english dominates 4 . although the number of non-english users is growing, underrepresented languages such as afrikaans, cebuano, and hindi still face a significant information gap @xcite . as shown by @xcite in their case study on wikipedia's entity-centric tables, information in wikipedia infoboxes @xcite is heavily skewed toward high-resource languages such as english, spanish, and french. they found that tables in low-resource languages often lack key information and are frequently outdated or inaccurate @xcite . this disparity is especially concerning in the digital age, where misinformation on widely accessible platforms can have far-reaching consequences. to address this, @xcite developed the infosync dataset to analyze information synchronization issues across 14 languages. they proposed aligning tables by matching similar keys and using rule-based methods to transfer and update information in tables across languages. figure 1 illustrates an example of information synchronization across multilingual tables (spanish and hindi). however, infosync's approach has a key limitation: rulebased methods become increasingly complex as new corner cases emerge, making generalization challenging. a more effective alternative is leveraging current large language models (llms) for zero-shot prompting, providing an easily scalable solution for these tasks. with new and advanced llms (such as gpt, mitral, llama @xcite @xcite ) being released every year, the zero-shot prompting capabilities of these models are improving with each new training iteration. these llms are consistently approaching, and in some cases surpassing, human performance across various nlp ap-figure 1 : example of information synchronization across multilingual tables. a reference table in a high-resource language is used to update outdated input tables in a low-resource language, resulting in an updated output table in the low-resource language. to investigate these questions, we first construct an information updation dataset. this dataset simulates the information updating task by using outdated versions of tables to represent old information, and comparing them with the latest versions that have been manually corrected to address any missing or outdated content. additionally, we assess the performance of state-of-the-art large language models (llms), specifically gpt-4 @xcite , for the task of information synchronization in entity-centric tables across multiple languages. to evaluate performance on the information updation task, we propose novel automated metrics that offer valuable insights into model performance and identify potential areas for improvement in future iterations. our initial experiments reveal that relying on a single prompt for multilingual information synchronization with llms yields suboptimal performance, frequently producing incoherent edits. to enhance these results, we propose a task decomposition approach. our proposed method uses multiple prompts to address smaller, more manageable subtasks, which are then connected in a sequential pipeline to generate the final output. task decomposition has shown promise in improving performance across a range of complex nlp applications @xcite @xcite , and we find it similarly beneficial in our work. our work makes the following contributions: code and dataset are available at https://zeroshot-llm-infosync.github.io/zero-shot-llminfosync/ . ## proposed methodology information synchronization for wikipedia infoboxes involves updating outdated rows in the table by conditionally modifying attributes, values, or both, using data from reference infoboxes(which have updated information). consider a source table (t s ) in language (l i ) that contains missing or outdated information. we also have a reference table (t r ) in language (l j ), which provides the missing and updated information not found in t s . additionally, we assume access to gold-standard updated table (t g ) in language (l i ), which can be regarded as having been manually curated. the information synchronization task is to update the table t s using only the information avail-able in t s and t r , with the goal of matching the updated table to t g . previous work by @xcite approaches this problem with a two-step methodology: (i) information alignment, which involves identifying similar rows across different tables using cosine similarity, and (ii) information update, which utilizes rule-based methods to update t s . in contrast, we propose to solve this task using large language models (llms) to provide a more automated and sophisticated solution, bypassing the need for elementary similarity measures and rule-based methods. we propose solving the information synchronization problem using various prompts, as outlined below: simple prompt. with large-scale pre-training of language modeling, new language models such as gpt4, llama, and gemini pro support promptbased (instruction set) evaluations, which do not require any finetuning. as a baseline prompt, we explain the task of information synchronization in the prompt giving details of types of missing information that might be presented between the two tables such as outdated information, missing information, or inconsistent information. the model is tasked to create an output table that has updated information from both source and reference tables. here, we ask the model to give more importance to reference table information while creating an updated output table. elementary task decomposition within a single prompt: to test whether a single prompt can give reasonable outputs even when directed to do task decomposition as an intermediate step, we propose align-update decomposition prompt. in this prompt, the model is instructed to first implicitly align all corresponding information between the two tables. once these alignments are automatically generated, the model should carefully review each alignment to identify and remove any outdated information wherever necessary. additionally, the model is explicitly instructed to add missing rows that could not be mapped during the alignment process. this prompt is inspired by the task decomposition of @xcite , which does the same with rule-based approach. hierarchical task decomposition prompt. instead of creating a single instruction set for the task of information synchronization. we do a hierarchical decomposition of the task and create prompts for each step. these prompts are applied sequentially, with the output of the last prompt as input to the next prompt in the hierarchy. different hierarchical steps for this prompt are: from different languages are converted to english. english is selected as the base language because most state-of-the-art llms are largely trained on curated english data, resulting in higher accuracy for complex reasoning and analysis tasks performed in english compared to other languages. after the update step, the revised knowledge graphs are converted back into tables. these tables are then translated back into the original languages of the source tables. we compare these three prompt designs for the task of information synchronization with relevant ablations for heirarchical task decomposition. prompt examples are shown in appendix b. ## infoupdate benchmark khincha et al. (2023) concentrated on developing a dataset for information alignment tasks, i.e., aligning similar keys across tables coming from different languages. they employed a rule-based method for updates and conducted human evaluations based on these updates to recommend edits on wikipedia pages. however, they did not create a dataset or propose an automated method for evaluating approaches to information updation. to address this, we introduce a new human-annotated dataset (infoupdate) for the information updation task focused on wikipedia infoboxes. this dataset comprises approximately 950 annotated instances across 9 categories: album, athlete, city, college, company, country, musician, person, and stadium, spanning 14 languages including spanish, french, german, arabic, hindi, korean, russian, afrikaans, cebuano, swedish, dutch, turkish, and chinese. additional dataset statistics are shown in appendix ## infoupdate evaluation metric for evaluation, we first experimented with an approach similar to chiang and lee (2023), where we tasked the model with comparing the output table against a reference table and providing a score. however, this approach proved to be highly unstable, resulting in vastly different responses even in low-temperature settings. moreover, the model often overlooked multiple rows during evaluation, making it difficult to establish a consistent scoring system due to the subjective nature of the process. therefore, we propose our novel evaluation metric consisting of two main steps: (a) information alignment evaluation and (b) information update evaluation, aimed at addressing the aforementioned issues. ## experiments and results alignment models. for information alignment, we employ an ensemble of multiround voting methods that combine infosync-a deterministic alignment algorithm-with llm-based alignment utilizing few-shot chain-of-thought prompting with detailed instructions. we conducted three runs each with gpt-3.5 and gemini 1.5 flash pro, followed by majority voting to establish the llm alignment. the majority-voted alignments from both gpt-3.5 and gemini 1.5 flash pro are then integrated into infosync, using majority voting again to achieve the final alignment. we refer to this approach as the multi-voting scheme. updation models. for information updation, we utilize three large language models (llms) for our experiments: gpt-3.5, gemini 1.5 flash pro @xcite , and llama 3.0 (70b) (ai@meta, 2024). the first two are closed-source models accessible only via api, whereas the latter is an open-source model. these models were selected as they represent state-of-the-art performance or are at least close to it and fall within the computational budget of our project. we believe that our results will also be applicable to other closed and open-source llms. evaluation strategy. evaluating the efficacy of large language models (llms) involves two key steps: (a) information alignment, which prepares the tables for comparison, and (b) information evaluation, which assesses the semantics. we evaluate both of these as following: ## further discussion why zero-shot over few-shot ? our proposed method employs hierarchical decomposition to break the problem into multiple, simplified tasks, effectively eliminating the reliance on few-shot learning. moreover, using a few-shot approach may introduce bias, as the model could perform disproportionately well on categories selected as exemplars. to ensure that information synchronization remains unbiased and generalizable across various entity categories, we opted for a zero-shot setting. lastly, our preliminary tests revealed that the increased computational and financial costs associated with few-shot learning using gpt apis resulted in only marginal improvements, making it a less practical choice for our objectives. why translation to english? translation models for low-resource languages like afrikaans and cebuano often struggle with accuracy. our research shows that direct translations between these languages tend to be unreliable, but translating everything into english boosts performance significantly. to address cultural nuances, we employ a two-way translation strategy. for reverse translations, we include examples from the original english-to-x translations, formatted as x-to-english mappings, as few-shot prompts. this approach helps capture cultural context and ensures more accurate alignment without losing meaning, especially for complex terms and idioms. this trend aligns with the fact that large language models (llms) are predominantly trained on english data. although multilingual models continue to improve, llms trained primarily with english datasets still excel at tasks like knowledge graph construction, data merging, and table generation. translating content into english allows us to leverage these llms' capabilities, improving semantic accuracy, consistency, and cross-lingual data processing. additional discussion on explanation for using knowledge graphs in the above proposed methods is explained in appendix section a.4, showing an example. ## related works multilingual information alignment and update. past efforts in multilingual table attribute alignment have employed both supervised and unsupervised techniques. supervised methods used simple classifiers based on features such as crosslanguage links and cosine text similarity derived from tables @xcite @xcite . on the other hand, unsupervised approaches relied on corpus statistics and template or schema matching for alignment @xcite . previous research on information updates @xcite @xcite has primarily focused on wikipedia and news articles, rather than semistructured data like tables. @xcite , specifically, examines the challenge of updating multilingual news articles across different languages. the most closely related work is @xcite , which proposed a rule-based approach. however, this method struggles with corner cases and does not leverage current state-of-the-art large language models (llms) for multilingual information synchronization. our work addresses these gaps by introducing an llm-based prompting approach that is adaptable across different languages, providing a more scalable solution. temporal understanding. temporal evolving information has been explored through various datasets in the context of question answering. torque @xcite and time-sensitiveqa @xcite focus on timesensitive questions from wikipedia, while sygma @xcite , cronquestions @xcite , and tempquestions @xcite deal with temporal queries in knowledge graphs. sumie @xcite , addresses a similar task in a specific domain and shares some similarities with our work. sumie deals with textual summarization, analyzing unstructured text. in contrast, our approach focuses on semistructured data, specifically the synchronization of infobox tables. this distinction allows us to tackle a broader range of synchronization tasks that require structured reasoning, beyond just textual content. our dataset and approach are multilingual, concentrating on the synchronization of tables across different languages-an aspect not covered by sumie. we investigate how data can be aligned and updated across multiple languages, whereas sumie does not explore multilingual contexts. sumie generates data using llm-based synthetic pipelines, whereas our dataset is directly sourced from realworld wikipedia data, offering more diversity. situatedqa @xcite and tem-plama @xcite target open-domain and cloze-style temporal queries. temptabqa @xcite , tiq @xcite , tram @xcite , and the big-bench project @xcite address temporal reasoning over tables and knowledge bases. more recent work @xcite investigates temporal reasoning in large language models (llms) using unstructured and synthetic data. however, none of this work focuses on editing multilingual tables. some studies focus on wikipedia-based document editing @xcite @xcite , but not tables. others apply editing strategies to technical, scientific, legal, and medical tables @xcite . expanding our approach to include social, economic, and cultural aspects in table updates would be a valuable direction for future research. ## conclusion and future work in this paper, we explored the application of large language models (llms) for multilingual information synchronization, focusing on improving the accuracy and coherence of updates to wikipedia tables in low-resource languages. our task decomposition strategy significantly outperformed baseline methods, especially in information updating and addition. the information updation dataset enabled a more precise evaluation of llm capabilities. overall, our findings highlight the potential of llms for dynamic data enrichment across diverse architectures, advancing multilingual and low-resource information systems. future research could explore several key directions: (a) extending the dataset to include diverse languages and more complex information structures to test llm generalizability, (b) integrating llms with rule-based methods or knowledge graphs for improved factual accuracy, (c) enhancing the model's performance in deletion tasks without weakening its strength in addition and updating, and (d) investigating efficient prompting strategies and fine-tuning techniques to improve scalability and real-world applicability across different model architectures.