arXiv:2608. 14737v1 Announce Type: cross Abstract: This study analyses LLMs in imbalanced binary classification, using study screening in systematic reviews as the application domain.
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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.
The study compared human and large language model (LLM) workflows for title‑and‑abstract screening in a complex scoping review. Human reviewers and two GPT‑5.4 file‑batch runs retained 42.2‑45.0% of records with 82.3‑82.9% recall, while Gemini 3.1 achieved the highest recall (83.9%) but retained 56.7% of records. Identical GPT‑5.4 runs showed 91.7% agreement yet differed on 94 records, including 29 verified eligible ones.
The paper introduces PRISMA-LLM, a reporting framework for AI-assisted systematic reviews. It is based on an analysis of 888 review-automation papers, showing a shift toward LLM- and software-driven workflows and inconsistent reporting of evaluation and limitations. The framework separates implementation details from consequence-sensitive evaluation and limitation reporting.
arXiv:2606. 19345v1 Announce Type: cross Abstract: The rapid increase in scientific publications leads to the fact that manual study screening in systematic literature reviews (SLRs) is increasingly resource consuming, inefficient, and inconsistent.
arXiv:2606. 10315v1 Announce Type: cross Abstract: LLM-as-judge is the default instrument for evaluating conversational agents, yet its reliability is almost always reported as agreement with human ratings, not recall of real defects.
The paper evaluates large language models (LLMs) as data quality annotators on two e-commerce tasks: entity matching and brand mislabeling. In entity matching, a simple rule-based baseline matched the LLM’s zero-shot performance (F1≈0.95), and a few-shot prompt actually lowered performance, highlighting the risk of small-sample prompt tuning. For brand mislabeling, the LLM outperformed a naive rule baseline (F1 0.833 vs 0.721) by leveraging background knowledge, and demonstrated high consistency across repeated runs (99.7% agreement).