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LLMs aid low-resource language IR dataset creation, but cross-lingual reuse faces challenges

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a BETA-labeling framework to construct multilingual datasets for low-resource information retrieval (IR). This framework utilizes multiple large language models (LLMs) with checks for consistency and majority agreement, followed by human evaluation to ensure label quality. The study also investigated the feasibility of reusing IR datasets from other low-resource languages through machine translation, revealing significant variations and semantic preservation issues that impact the reliability of cross-lingual dataset reuse. The findings highlight both the potential and limitations of LLM-assisted dataset creation for low-resource IR, offering guidance for building more dependable benchmarks. AI

IMPACT Provides methods to improve AI model performance in low-resource languages, potentially expanding AI accessibility.

RANK_REASON Academic paper detailing a new methodology for dataset construction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs aid low-resource language IR dataset creation, but cross-lingual reuse faces challenges

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Academic paper detailing a new methodology for dataset construction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Md. Najib Hasan, Mst. Jannatun Ferdous Rain, Fyad Mohammed, Nazmul Siddique ·

    BETA-Labeling for Multilingual Dataset Construction in Low-Resource IR

    arXiv:2602.14488v3 Announce Type: replace-cross Abstract: IR in low-resource languages remains limited by the scarcity of high-quality, task-specific annotated datasets. Manual annotation is expensive and difficult to scale, while using large language models (LLMs) as automated a…

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