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Bridging the gap: A survey of document retrieval techniques for high-resource and low-resource languages
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“applying RAG to lowresource languages presents unique challenges, including poor embedding representations, low retrieval quality, and semantic gaps caused by the scarcity of digital documents.”
From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Kazi et al., 2025
In the citing paper
“the scarcity and uneven quality of digital corpora reduce retrieval recall and coverage (Kazi et al., 2025).”
From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Kazi et al., 2025
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Metadata
- Authors
- Kazi, S., Khoja, S., & Daud, A.
- Affiliations
- Institute of Business Administration Karachi (PK)
- Rabdan Academy (AE)
- Corresponding author
- Daud, Ali
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 57
- Pages
- 100756
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W4409482736
- Cited by
- 9
- References
- 220
- Citations by year
- 2026: 6 · 2025: 3
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computer Science Review (publishedVersion)
- Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- document retrieval, low-resource languages, high-resource languages, cross-lingual information retrieval, domain adaptation, hybrid retrieval, neural ranking, morphological complexity, orthographic variation, data scarcity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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