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LLM screening workflows show variable performance in research synthesis · arXiv paper

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates the effectiveness of Large Language Models (LLMs) in screening research papers for evidence synthesis. The research found that while no workflow, human or LLM, could identify all relevant studies, LLM performance varied significantly based on the processing configuration rather than the model itself. The study suggests that LLMs are best suited for validated, human-supervised workflows in high-recall scenarios, rather than autonomous exclusion, due to issues like run-to-run consistency. AI

IMPACT LLM screening performance is highly dependent on workflow configuration, suggesting a need for careful integration into human-supervised research processes.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM screening workflows show variable performance in research synthesis · arXiv paper

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The cluster contains an academic paper detailing research findings on LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nikol Figalov\'a, Lynn Huestegge, Anne B\"ockler-Raettig ·

    Evaluating human and LLM screening workflows in a conceptually complex scoping review: Recall--workload trade-offs and run-to-run consistency

    arXiv:2608.26885v1 Announce Type: new Abstract: Background. Large language models (LLMs) are increasingly used for screening in evidence synthesis, where false negatives can remove relevant studies before full-text assessment. We compared human and LLM title-and-abstract screenin…

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