arXiv AI
TiAb Review Plugin: A Browser-Based Tool for AI-Assisted Study Selection in Systematic Reviews
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arXiv Computation and Language
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A Benchmark Framework for Screening Automation in Systematic Reviews
The paper introduces SRBench, a benchmark dataset comprising 45,064 labeled entries from 32 curated secondary studies, designed to evaluate large language model performance in systematic review screening while addressing class imbalance. It also presents PromptSR, a tool that facilitates prompt experimentation, experiment management, and result analysis for LLM-based screening. A use case demonstrates the practical application of both SRBench and PromptSR.
arXiv AI
Sep 10
SciLitBench: Benchmark and Design Principles for LLM-Powered Systematic Literature Reviews
SciLitBench is a multi-stage benchmark for evaluating large language models (LLMs) in systematic literature reviews, covering title and abstract screening, full-text screening, and schema-guided data extraction across 42,981 records and 888 included papers. The study shows that explicit inclusion/exclusion criteria boost title and abstract screening performance by 28.8% and researcher-authored rationales improve full-text screening by 15%. Data extraction performance varies widely, with high accuracy for publication year but low overlap for computational approaches, and even the best models recover only a fraction of annotated evidence and limitations.
arXiv AI
Aug 13
A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
arXiv AI
Jun 30
meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis
arXiv:2606. 28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points.
arXiv Machine Learning
Aug 18
Auxiliary uncertainty signals for LLM-assisted systematic review screening: a benchmark across eight Cohen drug-class reviews
arXiv:2608. 14551v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty.
arXiv Computation and Language
Sep 23
ABAI at COLIEE 2026 Task 1: Multi-Stage Retrieval with GraphRAG-Enhanced Meta-Learning, and a Post-Hoc Study of the Cross-Validation-to-Test Gap
The paper reports the ABAI submission to COLIEE 2026 Task 1, a case law retrieval challenge that suppresses cited passages, and details a four‑stage retrieval pipeline: multi‑view BM25 with reciprocal rank fusion, neural reranking, graph‑based features via a graph attention network, and a LightGBM meta‑learner over 34 features. The best run achieved an F1 score of 0.177 on the official test set, compared to a cross‑validated 0.311, and the authors attribute the gap to a recall ceiling, temporal distribution shift, and threshold miscalibration. A controlled post‑hoc study examined the impact of threshold transfer, decision quality across time, and query similarity, and identified specific remedies—such as BM25 length‑normalisation tuning, event‑triple views, and dense fusion—that improved recall, while other interventions had no effect.