Evaluating robustness of LLMs in question answering on multilingual noisy OCR data

Piryani, Bhawna (author), Mozafari, Jamshid (author), Abdallah, Abdelrahman (author), Doucet, Antoine (author), Jatowt, Adam (author)

Optical Character Recognition (OCR) plays a crucial role in digitizing historical and multilingual documents, yet OCR errors - imperfect extraction of text, including character insertion, deletion, and substitution can significantly impact downstream tasks like question-answering (QA). In this work, we conduct a comprehensive analysis of how OCR-induced noise affects the performance of Multilingual QA Systems. To support this analysis, we introduce a multilingual QA dataset MultiOCR-QA, comprising 50K question-answer pairs across three languages, English, French, and German. The dataset is curated from OCR-ed historical documents, which include different levels and types of OCR noise. We then evaluate how different state-of-the-art Large Language Models (LLMs) perform under different error conditions, focusing on three major OCR error types. Our findings show that QA systems are highly prone to OCR-induced errors and perform poorly on noisy OCR text. By comparing model performance on clean versus noisy texts, we provide insights into the limitations of current approaches and emphasize the need for more noise-resilient QA systems in historical digitization contexts.

• Language: English
• Keywords: multilingual QA, OCR text, large language models
• Work type: Other
• Typology: 1.08 - Published Scientific Conference Contribution
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 2366-2376
• PID: 20.500.12556/RUL-181096
• UDC: 004.85:004.352.242:81'322
• DOI: 10.1145/3746252.3761295
• COBISS.SI-ID: 272786691
• Publication date in RUL: 25.03.2026

Record is a part of a monograph

• Title: CIKM ’25 : proceedings of the 34th ACM International Conference on Information and Knowledge Management
• Place of publishing: New York (NY)
• Publisher: The Association for Computing Machinery
• ISBN: 979-8-4007-2040-6
• COBISS.SI-ID: 272764675

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: večjezično zagotavljanje kakovosti, optično prepoznavanje besedila, veliki jezikovni modeli

Projects

• Funder: EC - European Commission
• Project number: 101186647
• Name: Centre of Excellence in Artificial Intelligence for Digital Humanities
• Acronym: AI4DH