Search Results for author: Matteo Cancellieri

Found 2 papers, 1 papers with code

CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering

1 code implementation6 Jul 2023 David Pride, Matteo Cancellieri, Petr Knoth

CORE-GPT's performance was evaluated on a dataset of 100 questions covering the top 20 scientific domains in CORE, resulting in 100 answers and links to 500 relevant articles.

Question Answering

Predicting article quality scores with machine learning: The UK Research Excellence Framework

no code implementations11 Dec 2022 Mike Thelwall, Kayvan Kousha, Mahshid Abdoli, Emma Stuart, Meiko Makita, Paul Wilson, Jonathan Levitt, Petr Knoth, Matteo Cancellieri

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics.

Active Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.