The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.
By Valentin Romanov, Monique Bax, Steven Niederer
The paper evaluates browser-based large language models (LLMs) for extracting detailed, contextualized data from scientific papers. It presents four workflows: (1) expert-curated prompts yield good extraction but struggle with nuance; (2) LLMs can generate effective prompts from simple instructions; (3) autonomous literature discovery is challenging, with missing or hallucinated references; (4) LLMs can build new datasets from guidelines that align closely with human experts, yet still need human oversight. The study outlines a practical, auditable workflow where experts set standards, models cross-check extractions, and researchers resolve disputes, enabling scalable scientific data curation.
The study evaluates literature reviews produced by large language models (LLMs) using short and long context windows, assessing their quality across 15 dimensions. Results show that while larger context windows allow LLMs to incorporate more information and maintain coherence, they also increase repetition, omission of key works, and a tendency toward descriptive rather than synthetic content. Human oversight remains essential for meeting academic publishing standards, and the authors suggest future work should blend human expertise with AI to mitigate these limitations.
By Muhammad Ali Chaudhry, Xinyuan Hao, Haifa Alwahaby
arXiv:2608.21374v1 Announce Type: new
Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
By Ruotong Zhao, Zhiyu Chen, Xurui Liu, Haidong Xue, Dong Liang, Jigao Fu, Wu YanBiao, Yuanyi Zhen, Fengli Xu, Yong Li
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.
By Miguel Zabaleta, Baihan Lin
arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.
By Vaibhava Lakshmi Ravideshik, Mayank Kejriwal