1 citations · 3 across the 6 of their papers we have counts for
6 papers
Scoring Grant Applications with Large Language Models
Mike Thelwall, David Pride, Francesco Osborne +4
Purpose: Assessing grant applications is time-consuming and difficult, adding to the overall burden of academic peer review. Whilst funders are exploring whether AI can help, there…
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Tanay Aggarwal, Angelo Salatino, Francesco Osborne +1
Knowledge Organization Systems like Ontologies and taxonomies are fundamental for structuring scientific knowledge, yet their manual curation presents a persistent bottleneck in kn…
How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders
Michael McCoubrey, Angelo Salatino, Francesco Osborne +1
In recent years, there has been a growing use of generative AI, and large language models (LLMs) in particular, to support both the assessment and generation of scientific work. Al…
A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Alessia Pisu, Livio Pompianu, Francesco Osborne +3
Taxonomies and ontologies of research topics (e.g., MeSH, UMLS, CSO, NLM) play a central role in providing the primary framework through which intelligent systems can explore and i…
Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study
Tanay Aggarwal, Angelo Salatino, Francesco Osborne +1
Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient classification, dissemination and retrieval…
Modelling and Classifying the Components of a Literature Review
Francisco Bolaños, Angelo Salatino, Francesco Osborne +1
Previous work has demonstrated that AI methods for analysing scientific literature benefit significantly from annotating sentences in papers according to their rhetorical roles, su…