A new benchmark for structured retrieval-augmented generation (RAG) in perovskite photovoltaics has been developed, focusing on quantitative answers derived from experimental data rather than qualitative text overlap. This benchmark utilizes a dataset of over 45,000 device records and passages from more than 25,000 papers. When tested with six retrieval architectures and a 14B model, standard passage retrieval achieved only 16-23% accuracy, while RAG models equipped with read-only SQL execution over a structured device table reached over 51% accuracy. This highlights the necessity of systematic table extraction for large-scale quantitative research in materials science. AI
IMPACT Demonstrates the critical need for structured data extraction and SQL capabilities to enable quantitative research in scientific literature.
RANK_REASON The item describes a new benchmark and research findings for a specific scientific domain, not a frontier model release or significant industry event. [lever_c_demoted from research: ic=1 ai=1.0]
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