KoVRE: Training an Efficient Embedding Model for Korean Visual Document Retrieval

KoViDoRe: A Benchmark for Korean Visual Document Retrieval

Recent advances in multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. However, existing benchmarks remain largely centered on English and provide limited coverage of Korean visual documents with complex structures. Furthermore, most existing Korean resources primarily evaluate single-page retrieval, failing to capture realistic scenarios that require evidence aggregation across multiple pages. To address these gaps, we introduce KoViDoRe, a benchmark for Korean visual document retrieval. The dataset is constructed from publicly available Korean documents with diverse layouts, including tables, figures, and multi-column structures. We develop a multi-stage data curation pipeline consisting of structured document parsing, synthetic query generation using both summary-based and context-based strategies, and relevance mapping with human verification. Using KoViDoRe, we evaluate a wide range of multimodal retrieval models and observe that current models struggle to effectively handle Korean visual document retrieval, particularly in settings involving structured content and diverse query types. Motivated by this finding, we further curate a large-scale training dataset, Ko-VDR Train Public, to support the development of retrieval models tailored to Korean visual documents. Together, KoViDoRe and Ko-VDR Train Public provide a unified benchmark and training resource for Korean visual document retrieval.

MoganColBERT-TR: A Late-Interaction Multi-Vector Retrieval Model for Turkish

We previously reported a ModernBERT encoder trained from scratch for Turkish (MoganBERT-TR) and a single-vector embedding model built on top of it (MoganBERT-embed). This work introduces the third model in that lineage: MoganColBERT-TR, a multi-vector retrieval model that, instead of compressing a query or a document into a single vector, represents it at the token level through a 768->128 projection and scores it with MaxSim late interaction. The model is not trained from scratch: the embedding model's encoder is taken as the starting point and adapted to the ColBERT objective with a single-epoch distillation phase. Training data is produced from two sources - title-to-passage pairs carved out of our own pretraining corpus in the character domain and at sentence boundaries, and two Turkish question-based retrieval sets - and is distilled from the soft scores of a cross-encoder teacher (bge-reranker-v2-m3) over one positive and seven mined negatives. We show that in hard negative mining, rank-based skipping alone is insufficient and must be combined with a group mask and a cosine ceiling. Evaluation is carried out with the official pipeline of TurkColBERT, a benchmark built for Turkish late-interaction retrieval (PLAID index, exact MaxSim), on five Turkish BEIR datasets; none of them appears in our training pool, so all five results are clean zero-shot. With 148.9M parameters, MoganColBERT-TR reaches an overall score of 37.36 (35.53 nDCG@100, 31.81 nDCG@10) averaged over the five datasets and finishes second among the five models compared: it outperforms the twice-as-large ColmmBERT-base-TR on four of five datasets and by +3.05 overall, and the benchmark's largest model by +12.30. The gap to the leading model (mLateOn) is concentrated on ArguAna-TR, the dataset with by far the longest queries.

H+ Embedding: Harmonizing Global and Token-Level Retrieval with Context-Dependent Phrases

H+ Embedding is introduced, a unified multi-granularity retriever that predicts variable-length phrase partitions, preserves uncovered tokens as singletons, and applies importance-guided unit selection with weighted MaxSim interaction.

Cross-Lingual Information Retrieval for Indonesian–Javanese Documents Using LLM-Based Reranking

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.

DocuMind: An Offline Multilingual Retrieval-Augmented Generation System for Document Question Answering Using Local Large Language Models

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.

Optimizing Multilingual Embedding Models for Retrieval and Reranking in RAG Pipelines: Enhancing Semantic Search in Turkish Medical Datasets