# LLAssist: Simple Tools for Automating Literature Review Using Large Language Models

arXiv: https://arxiv.org/abs/2407.13993 · HTML: https://mlsanity.com/paper/2407.13993/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [LitLLM: A Toolkit for Scientific Literature Review](https://mlsanity.com/paper/2402.01788/) (2024)
- [A Comparative Study of Programming Languages in Rosetta Code](https://mlsanity.com/paper/1409.0252/) (2014)
- [PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models](https://mlsanity.com/paper/2306.14905/) (2023)
- [Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2407.10652/) (2024)

## Built on by (most cited first, 4 of 4)

- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [Contextualized AI for Cyber Defense: An Automated Survey using LLMs](https://mlsanity.com/paper/2409.13524/) (2024)
- [Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2510.11409/) (2025)
- [Local AI pre-screening for human triple-blind peer review in health sciences](https://mlsanity.com/paper/2608.14625/) (2026)

## Abstract (arXiv)

This paper introduces LLAssist, an open-source tool designed to streamline literature reviews in academic research. In an era of exponential growth in scientific publications, researchers face mounting challenges in efficiently processing vast volumes of literature. LLAssist addresses this issue by leveraging Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to automate key aspects of the review process. Specifically, it extracts important information from research articles and evaluates their relevance to user-defined research questions. The goal of LLAssist is to significantly reduce the time and effort required for comprehensive literature reviews, allowing researchers to focus more on analyzing and synthesizing information rather than on initial screening tasks. By automating parts of the literature review workflow, LLAssist aims to help researchers manage the growing volume of academic publications more efficiently.