SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature ReviewsSciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews

Computer Science > Artificial Intelligence

[Submitted on 29 Aug 2026]

Title:SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews

View PDF HTML (experimental)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.

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