Can Large Language Models Review the Scientific Literature? A Test Against a 102-Article Benchmark

Authors: Abdullah Al Kalbani , David M. Schultz , Alexandra K. Anderson-Frey , Zhonghua Zheng , Catherine McGuire

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Abstract: Large language models (LLMs) are increasingly used to support scientific literature reviews, yet evaluations often examine individual tasks rather than the review process as a whole. We assess six AI tools across four stages: finding relevant articles, interpreting them, synthesizing across them, and proposing future research. The benchmark is a published review of the Spanish plume weather pattern and its 102-article corpus.

In the initial searches, five tools collectively identified 57 articles, with the best individual tool finding 42. Institutional library access increased discovery from 42 to 82 articles for Codex and from 15 to 79 for Claude Code. Given the complete corpus of 102 PDFs, three tools correctly identified all 18 articles discussing the Spanish plume substantially, but incorrectly classified 40% to 79% of the 84 passing mentions as substantial. The same three tools produced syntheses evaluated blindly by two domain experts. Although largely accurate, the syntheses provided limited insight and sometimes contained unsupported claims, quotation errors, and exaggerated statements of consensus. Finally, all three tools proposed research directions broader than the Spanish plume literature, although two approached questions identified in the benchmark review.

No single tool performed best across all tasks. We provide eight recommendations for researchers using AI tools in literature reviews. Our findings suggest that LLMs can assist with discovering and interpreting scientific literature, particularly when institutional access is available, but cannot replace expert judgment in evaluating evidence or synthesizing research.

Comments:

34 pages, 7 figures, 6 tables. Data, prompts, analysis scripts and results: this https URL (GitHub: this https URL )

Subjects:

Artificial Intelligence (cs.AI) ; Digital Libraries (cs.DL)

MSC classes:

68T50, 68P20, 68T07

Cite as:

arXiv:2611.05459 [cs.AI]

(or

arXiv:2611.05459v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05459

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arXiv-issued DOI via DataCite

Submission history

From: Abdullah Al Kalbani [ view email ]

[v1]

Tue, 3 Nov 2026 18:57:20 UTC (811 KB)

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