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

arXiv: https://arxiv.org/abs/2307.04683 · HTML: https://mlsanity.com/paper/2307.04683/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)

## Built on by (most cited first, 10 of 10)

- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://mlsanity.com/paper/2505.14279/) (2025)
- [Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending](https://mlsanity.com/paper/2401.16458/) (2024)
- [Large Language Models as Evaluators for Scientific Synthesis](https://mlsanity.com/paper/2407.02977/) (2024)
- [From Keywords to Structured Summaries: Streamlining Scholarly Information Access](https://mlsanity.com/paper/2402.14622/) (2024)
- [Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs](https://mlsanity.com/paper/2404.16405/) (2024)
- [Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers](https://mlsanity.com/paper/2508.03962/) (2025)
- [Charting the Future of Scholarly Knowledge with AI: A Community Perspective](https://mlsanity.com/paper/2509.02581/) (2025)
- [InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature](https://mlsanity.com/paper/2509.20493/) (2025)
- [Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach](https://mlsanity.com/paper/2512.16425/) (2025)

## Abstract (arXiv)

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.