CORE-GPT: Combining Open Access research and large language models for
  credible, trustworthy question answering
David Pride Matteo Cancellieri Petr Knoth
In this paper, we present CORE-GPT, a novel question-answering platform that
combines GPT-based language models and more than 32 million full-text open
access scientific articles from CORE. We first demonstrate that GPT3.5 and GPT4
cannot be relied upon to provide references or citations for generated text. We
then introduce CORE-GPT which delivers evidence-based answers to questions,
along with citations and links to the cited papers, greatly increasing the
trustworthiness of the answers and reducing the risk of hallucinations.
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. The quality of the provided answers and and relevance of the
links were assessed by two annotators. Our results demonstrate that CORE-GPT
can produce comprehensive and trustworthy answers across the majority of
scientific domains, complete with links to genuine, relevant scientific
articles.