发表机构
University of Luxembourg; King Fahd University of Petroleum and Minerals（卢森堡大学; 法赫德国王石油矿产大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究推出LëtzCross基准，对比纯文本与ColPali式页面图像检索器，发现后者表现更优，还明确单语言微调中法语文对卢森堡语查询表现最高、多语言微调中纳入卢森堡语效果最强。
AI 中文摘要
近期，ColPali等页面图像检索器提升了对视觉丰富文档的检索效果，但人们对其在跨语言低资源场景中的表现知之甚少。我们推出LëtzCross——一个针对卢森堡语PDF文档的跨语言页面级检索基准，文档页面以图像形式索引，查询提供为英语、法语、德语和卢森堡语。该基准结合了面向文本的问答对与视觉基础问答对，覆盖了基于PDF的检索增强生成（RAG）中的文本与视觉检索需求。我们使用LëtzCross对比基于光学字符识别（OCR）的纯文本检索器与ColPali式页面图像检索器，发现后者在该系统级对比中对所有查询语言的表现均更优。我们还研究了单语言与多语言微调：微调可在查询语言间迁移，在单语言设置中，法语对卢森堡语查询的平均表现最高；在多语言设置中，纳入卢森堡语可获得最强结果，并大幅提升卢森堡语查询的检索效果。
英文摘要
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.
