[Submitted on 29 Aug 2026]
Title:SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews
View a PDF of the paper titled SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews, by Miguel Zabaleta and 1 other authors
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Abstract:Systematic reviews require sustained human judgment across thousands of records, yet existing evaluations of large language models (LLMs) typically examine review stages in isolation. We introduce SciLitBench, a multi-stage benchmark spanning title and abstract screening, full-text screening, and schema-guided data extraction, with 42,981 retrieved records, 1,012 full texts, and annotations for 888 included papers. Across 22 open-weight LLMs from six model families, explicit inclusion and exclusion criteria improve title and abstract screening $F_2$ by 28.8\%, while researcher-authored rationales improve full-text screening by 15\%. Data extraction reveals a different reliability regime: performance declines from 0.97 accuracy for publication year to 0.37 Jaccard overlap for computational approach, while the strongest models recover only 30\% of annotated evaluation evidence and 25\% of limitations. SciLitBench identifies a practical boundary between high-recall screening and evidence-complete extraction and provides a reproducible resource for evaluating LLM-assisted evidence synthesis.
Comments: 22 pages, 6 figures, and 5 tables. Data and code are available at this https URL
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.05505 [cs.AI]
(or arXiv:2609.05505v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.05505
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Miguel Zabaleta [view email] [v1] Sat, 29 Aug 2026 00:04:34 UTC (3,833 KB)
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View a PDF of the paper titled SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews, by Miguel Zabaleta and 1 other authors
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