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AI framework developed to track dataset usage in research literature

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new AI framework to track and classify dataset usage within academic literature, addressing a gap in current research infrastructure. This multitask GLiNER-based system jointly extracts dataset mentions, identifies relations, and classifies usage contexts. To overcome the challenge of limited labeled data, the methodology incorporates synthetic data generation and LLM-based revalidation to improve accuracy and consistency in monitoring dataset citations. AI

IMPACT Enhances transparency and reproducibility in research by enabling better tracking of dataset citations.

RANK_REASON The cluster contains an academic paper detailing a new methodology and framework for monitoring dataset usage in research literature. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

AI framework developed to track dataset usage in research literature

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The cluster contains an academic paper detailing a new methodology and framework for monitoring dataset usage in research literature. [lever_c_demoted from research: ic=1 ai=1.0]
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117 days old
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rafael Macalaba, Aivin V. Solatorio ·

    AI for Monitoring and Classifying Data Used in Research Literature

    arXiv:2605.30582v1 Announce Type: new Abstract: While platforms like Google Scholar and Semantic Scholar track citations for academic papers, no comparable infrastructure exists for monitoring dataset usage in research literature, leaving the landscape of data use largely opaque.…

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