# NeuroLit Navigator: A Neurosymbolic Approach to Scholarly Article Searches for Systematic Reviews

arXiv: https://arxiv.org/abs/2503.00278 · HTML: https://mlsanity.com/paper/2503.00278/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval](https://mlsanity.com/paper/2307.00589/) (2023)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations](https://mlsanity.com/paper/2405.02228/) (2024)
- [FAST2: an Intelligent Assistant for Finding Relevant Papers](https://mlsanity.com/paper/1705.05420/) (2017)
- [Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field](https://mlsanity.com/paper/2407.14076/) (2024)
- [GEAR-Up: Generative AI and External Knowledge-based Retrieval Upgrading Scholarly Article Searches for Systematic Reviews](https://mlsanity.com/paper/2312.09948/) (2023)
- [Neurosymbolic AI for Enhancing Instructability in Generative AI](https://mlsanity.com/paper/2407.18722/) (2024)

## Abstract (arXiv)

The introduction of Large Language Models (LLMs) has significantly impacted various fields, including education, for example, by enabling the creation of personalized learning materials. However, their use in Systematic Reviews (SRs) reveals limitations such as restricted access to specialized vocabularies, lack of domain-specific reasoning, and a tendency to generate inaccurate information. Existing SR tools often rely on traditional NLP methods and fail to address these issues adequately. To overcome these challenges, we developed the ``NeuroLit Navigator,'' a system that combines domain-specific LLMs with structured knowledge sources like Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). This integration enhances query formulation, expands search vocabularies, and deepens search scopes, enabling more precise searches. Deployed in multiple universities and tested by over a dozen librarians, the NeuroLit Navigator has reduced the time required for initial literature searches by 90%. Despite this efficiency, the initial set of articles retrieved can vary in relevance and quality. Nonetheless, the system has greatly improved the reproducibility of search results, demonstrating its potential to support librarians in the SR process.