Problem
Research questions and friction points this paper is trying to address.
Cross-lingual
Multimodal Retrieval
Low-resource Settings
Luxembourgish Documents
LëtzCross: A Cross-Lingual Page-Level Benchmark for Multimodal Retrieval over Luxembourgish Documents
🤖 AI Summary
研究通过构建LëtzCross基准，对比了基于OCR的文本检索器与ColPali风格的页面图像检索器在卢森堡语文档上的表现，发现后者在多语言环境下表现更佳。
📝 Abstract
Recent page-image retrievers such as ColPali have improved retrieval over visually rich documents, yet little is known about how they behave in cross-lingual, low-resource settings. We introduce LëtzCross, a benchmark for cross-lingual page-level retrieval over Luxembourgish PDF documents, with document pages indexed as images and queries provided in English, French, German, and Luxembourgish. The benchmark combines text-focused QA pairs with visually grounded QA pairs, covering both textual and visual retrieval needs in PDF-based RAG. We use LëtzCross to compare OCR-based text-only retrievers with ColPali-style page-image retrievers and find that the latter perform better across query languages in this system-level comparison. We also examine single-language and multilingual fine-tuning. Fine-tuning transfers across query languages, with French yielding the highest mean performance on Luxembourgish queries among the single-language settings. In the multilingual setting, including Luxembourgish gives the strongest results and substantially improves retrieval for Luxembourgish queries.
Innovation
Methods, ideas, or system contributions that make the work stand out.
cross-lingual
page-level retrieval
multimodal
Luxembourgish
fine-tuning
🔎 Similar Papers
No similar papers found.
