# GEAR-Up: Generative AI and External Knowledge-based Retrieval Upgrading Scholarly Article Searches for Systematic Reviews

arXiv: https://arxiv.org/abs/2312.09948 · HTML: https://mlsanity.com/paper/2312.09948/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review](https://mlsanity.com/paper/2409.04600/) (2024)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [NeuroLit Navigator: A Neurosymbolic Approach to Scholarly Article Searches for Systematic Reviews](https://mlsanity.com/paper/2503.00278/) (2025)

## Abstract (arXiv)

Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources. Given the ever-increasing volume of published studies, applying existing computing and informatics technology can decrease this time and resource burden. Due to the revolutionary advances in (1) Generative AI such as ChatGPT, and (2) External knowledge-augmented information extraction efforts such as Retrieval-Augmented Generation, In this work, we explore the use of techniques from (1) and (2) for SR. We demonstrate a system that takes user queries, performs query expansion to obtain enriched context (includes additional terms and definitions by querying language models and knowledge graphs), and uses this context to search for articles on scholarly databases to retrieve articles. We perform qualitative evaluations of our system through comparison against sentinel (ground truth) articles provided by an in-house librarian. The demo can be found at: https://youtu.be/zMdP56GJ9mU.