{"title":"LLAssist: Simple Tools for Automating Literature Review Using Large Language Models","authors":"Christoforus Yoga Haryanto","doi":"arxiv-2407.13993","DOIUrl":null,"url":null,"abstract":"This paper introduces LLAssist, an open-source tool designed to streamline\nliterature reviews in academic research. In an era of exponential growth in\nscientific publications, researchers face mounting challenges in efficiently\nprocessing vast volumes of literature. LLAssist addresses this issue by\nleveraging Large Language Models (LLMs) and Natural Language Processing (NLP)\ntechniques to automate key aspects of the review process. Specifically, it\nextracts important information from research articles and evaluates their\nrelevance to user-defined research questions. The goal of LLAssist is to\nsignificantly reduce the time and effort required for comprehensive literature\nreviews, allowing researchers to focus more on analyzing and synthesizing\ninformation rather than on initial screening tasks. By automating parts of the\nliterature review workflow, LLAssist aims to help researchers manage the\ngrowing volume of academic publications more efficiently.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"80 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.13993","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.