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New MAPLE benchmark evaluates multi-aspect scientific paper retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MAPLE, a new benchmark designed to evaluate scientific paper retrieval systems. Unlike existing benchmarks that focus on single query-paper relevance, MAPLE assesses a retriever's ability to consistently find a paper based on its motivation, methods, and experimental findings. The benchmark includes 2,095 queries for recent machine learning and natural language processing papers, incorporating both textual and multimodal content. Experiments show a significant performance gap, with the best models struggling to retrieve papers across all aspects, highlighting the need for more comprehensive retrieval methods. AI

IMPACT This benchmark could drive the development of more sophisticated AI systems for scientific literature search and analysis.

RANK_REASON The item describes a new benchmark for evaluating scientific paper retrieval systems, which falls under research. [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 →

New MAPLE benchmark evaluates multi-aspect scientific paper retrieval

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The item describes a new benchmark for evaluating scientific paper retrieval systems, which falls under research. [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) · Yue Zhang ·

    Can Retrievers Find the Same Paper from Different Aspects? A Multi-Aspect Full-Paper Scientific Retrieval Benchmark

    Scientific papers contain multiple searchable facets such as background, methods. However, many paper retrieval benchmarks merely evaluate individual query-paper relevance, while overlooking other facets of the same paper. To bridge this gap, we introduce MAPLE, an expert-validat…

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