This work introduces L\"etzCross, 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, and examines single-language and multilingual fine-tuning.
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 L\"etzCross, 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 L\"etzCross 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.
The novel task of cross-modal query suggestion is introduced, which interactively guides users by suggesting textual refinements based on visual clusters identified in the retrieval results, and the creation of CroQS, a benchmark dataset comprising 50 diverse queries and 295 semantic clusters in generic domain.
Giacomo Pacini, Nicola Messina, Nicola Tonellotto et al.· 0 citations
Cross-lingual information retrieval (CLIR) for low-resource regional languages remains challenging due to limited annotated training data, morphological complexity, and linguistic diversity. This paper systematically investigates Indonesian–Javanese CLIR by integrating traditional sparse lexical retrieval, multilingual dense retrieval, and advanced large language model (LLM)-based reranking within a multi-stage retrieval framework. To evaluate our approach, comprehensive experiments are conducted on the Indonesian–Javanese subset of the CLIRMatrix benchmark, comprising 20,000 Javanese documents, 1,000 Indonesian queries, and 11,000 graded relevance judgments. We compare BM25 with query and document translation, multilingual bi-encoder retrieval, and various LLM-based pointwise reranking methods. The experimental results show that BM25 combined with document translation provides the strongest initial baseline, achieving a Mean Average Precision (MAP) of 0.45, Mean Reciprocal Rank (MRR) of 0.71, and nDCG@10 of 0.55. While LLM-based reranking consistently improves weaker dense retrieval baselines, it often degrades search performance when applied directly on top of strong BM25-based retrieval. To address this, Reciprocal Rank Fusion (RRF) is introduced, which mitigates this degradation issue and achieves the best overall performance, reaching a MAP of 0.47, MRR of 0.74, and nDCG@10 of 0.57. These empirical findings highlight the critical importance of robust initial retrieval baselines and demonstrate that LLM-based reranking is most effective when combined with rank fusion strategies in low-resource CLIR settings.
Raden Mohamad Adrian Ramadhan Hendar Wibawa, Ika Alfina, Evi Yulianti· International Conference on...· 0 citations
Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations that plain OCR linearizes, corrupts, or omits. ColPali-family visual retrievers address this with patch-level multi-vector indexes and late-interaction scoring, keeping image-derived retrieval on the query-time serving path. We introduce MIDR (Multimodal Indexing for Document Retrieval), a training-free framework for enrichment-augmented indexing that shifts multimodal reasoning to index time. During ingestion, a multimodal LLM converts rendered pages into verified textual fields that are indexed with BM25F and optionally fused with dense retrieval, enabling text-centric serving over multimodally grounded evidence. On ViDoRe V3, MIDR Hybrid achieves 0.6219 average nDCG across five English domains, a 23.0% relative gain over BM25, remaining competitive with ColQwen2.5. On two French-document domains, enrichment bridges English queries and French page text, lifting BM25 from 0.1532 to 0.5448 nDCG and outperforming ColQwen2.5. Across all seven domains, MIDR leads ColQwen2.5 on four while using approximately 9x smaller index memory and approximately 2x lower query latency. These results establish index-time multimodal reasoning as a compelling accuracy-deployment alternative to serving-time visual late interaction.
Debanjan Mahata, Atharva Tendle, Daniel Preoţiuc-Pietro et al.· 0 citations
This work introduces KoViDoRe, a benchmark for Korean visual document retrieval, and curates a large-scale training dataset, Ko-VDR Train Public, to support the development of retrieval models tailored to Korean visual documents.
Yongbin Choi, Yongwoo Song, Mujeen Sung· Proceedings of the 2nd Works...· 1 citation
DocuMind is a fully offline, privacy-preserving, multilingual Document Question Answering system built on the Retrieval-Augmented Generation (RAG) architecture, enabling true cross-lingual retrieval without any translation step.
S. S, Sripalreddy· International Scientific Jou...· 0 citations
KoVRE: Korean Visual Document Retrieval Embedding is introduced, a single-vector retriever for Korean visual documents, alongside a comprehensive training recipe, demonstrating that targeted bilingual supervision and carefully designed training strategies can produce a highly effective Korean VDR model across diverse document domains, without requiring a scaled-up backbone or multi-vector representations.
