This chapter surveys 34 peer-reviewed papers applying generative LLMs to literature retrieval and the screening of candidate studies against eligibility criteria, identified via a Boolean search over the OpenAIRE Graph.
Abstract
The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.
This systematic literature review (SLR) provides a comprehensive overview of pruning techniques applied to LLMs, based on 60 peer-reviewed studies and preprints published between 2022 and 2025, sourced from major digital libraries.
F. Bazikar, Atefeh Hemmati, Akram Reza et al.· Knowledge and Information Sy...· 0 citations
An LLM-based framework is proposed that leverages full-text key-insight extraction to enhance literature classification and implemented a confidence-weighted voting (CWV) mechanism using multiple LLMs to improve robustness.
Zihan Song, Shan Huang, Ngeemasara Thapa et al.· 0 citations
Large language models (LLMs) can ease the work of screening titles and abstracts for systematic reviews, but obtaining reliable results requires researchers to make practical choices about which LLMs to use, how to combine their scores into a ranking, and how far down that ranking to read. We aimed to identify a genera...
S. Spillias, Laura Avila-Turriago, C. Brown et al.· bioRxiv· 0 citations
This survey examines how these methods integrate graphs into various stages of the LLM pipeline, including the input, model, and output phases, and outlines the challenges and future research directions for developing more efficient and interpretable solutions.
Xin-Yan Zhu, Cheng Yang, Qiu-Yue Wang et al.· Proceedings of the Thirty-Fi...· 0 citations
Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern Scholarly Knowledge Graphs. The unstructured and highly heterogeneous nature of these documents has traditionally hindered their large-scale processing. In this demo pap...
Angelo Salatino, Francesco Osborne, Alexis Vizcaino et al.· 0 citations
The digital proliferation of scientific articles since the 1980s has made strategic reading an essential skill for researchers. To support automatic filtering, linking and analysis of scientific literature, fine-grained scientific content, extensive semantic links and machine-readable formats are required. This rev...
Mengjuan Weng, Xiaoguang Wang, Ning-Yuan Song et al.· Journal of Documentation· 0 citations
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