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Study highlights growing name collision issue in AI paper retrieval

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the growing problem of cross-topic name collisions in scientific paper retrieval, particularly within AI-related research. The paper, titled "PRISM" Is Not All You Need, found that names shared across different research areas are increasing significantly, making it harder for readers and retrieval systems to identify the correct paper. The research quantifies this ambiguity and explores how context and advanced ranking models can help resolve it, offering tools and datasets for further inspection. AI

IMPACT Highlights challenges in AI research discovery and the need for improved retrieval systems.

RANK_REASON Research paper published on arXiv detailing a specific problem in scientific information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

Study highlights growing name collision issue in AI paper retrieval

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Research paper published on arXiv detailing a specific problem in scientific information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yibo Kong ·

    "PRISM" Is Not All You Need: Cross-Topic Name Collisions and Scientific Paper Retrieval

    Scientific names increasingly connect papers from different research topics, leaving readers to recover a paper's identity from context. We study this convergence in 564,861 AI-related arXiv records. Cross-topic name collision grows 4.33-fold from a pooled 2020-2022 reference to …

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