Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· pp. 5256-5258· 2 citations· 28 references
Computer Science
TL;DR
It is argued that Nepali provides a compelling testbed for studying morphology-aware retrieval, code-mixed query processing, and multilingual embedding transfer.
Abstract
Despite being spoken by over 26 million people, Nepali lacks a standardized Information Retrieval (IR) benchmark, preventing systematic evaluation of retrieval models in this low-resource setting. While progress has been made in several Nepali NLP tasks, no publicly available test collection with queries and relevance judgments exists for ad-hoc retrieval task. We propose the creation of a first standardized Nepali IR benchmark supporting monolingual (Nepali queries \rightarrow Nepali documents), cross-lingual (English queries \rightarrow Nepali documents), and code-mixed (Nepali-English queries \rightarrow Nepali documents) retrieval. We argue that Nepali provides a compelling testbed for studying morphology-aware retrieval, code-mixed query processing, and multilingual embedding transfer.
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.
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CharBM25 is established as an effective, GPU-free retrieval strategy that matches or exceeds dense retrieval at negligible computational cost, and combining it with a general-purpose LLM of 12B+ parameters delivers reliable, training-free Devanagari post-OCR correction without task-specific fine-tuning.
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
Large language models (LLMs) have shown a strong ability to solve a wide range of language tasks. However, they became less practical for subject-specific applications, especially when dealing with low-resource languages. This paper shows an example of adaptation of small language models (SLMs) to solve subject-specifi...
G. Askar, A. Mutaikhan, M. Aitkozhayeva et al.· NEWS of National Academy of...· 0 citations
The main implication of this research is the validation of a practical architecture for improving IR systems, offering a viable alternative for domain-specific contexts such as Sequran.
Ray Ramadita, Wisnu Uriawan, W. Zulfikar· 0 citations
