LLAssist: Simple Tools for Automating Literature Review Using Large Language Models
- URL: http://arxiv.org/abs/2407.13993v2
- Date: Mon, 30 Sep 2024 13:03:13 GMT
- Title: LLAssist: Simple Tools for Automating Literature Review Using Large Language Models
- Authors: Christoforus Yoga Haryanto,
- Abstract summary: LLAssist is an open-source tool designed to streamline literature reviews in academic research.
It uses Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to automate key aspects of the review process.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- A Survey of Small Language Models [104.80308007044634]
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources.
We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
arXiv Detail & Related papers (2024-10-25T23:52:28Z) - PROMPTHEUS: A Human-Centered Pipeline to Streamline SLRs with LLMs [0.0]
PROMPTHEUS is an AI-driven pipeline solution for Systematic Literature Reviews.
It automates key stages of the SLR process, including systematic search, data extraction, topic modeling, and summarization.
It achieves high precision, provides coherent topic organization, and reduces review time.
arXiv Detail & Related papers (2024-10-21T13:05:33Z) - Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science.
Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords.
We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
arXiv Detail & Related papers (2024-10-08T05:13:27Z) - NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit [9.621564860645513]
We propose a novel framework that leverages Natural Language Processing (NLP) techniques.
This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain.
We introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature.
arXiv Detail & Related papers (2024-09-10T05:41:40Z) - LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [106.45895712717612]
Large language models (LLMs) have shown remarkable versatility in various generative tasks.
This study focuses on the topic of LLMs assist NLP Researchers.
To our knowledge, this is the first work to provide such a comprehensive analysis.
arXiv Detail & Related papers (2024-06-24T01:30:22Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is a large language model-powered research idea writing agent.
It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers.
SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level.
Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
arXiv Detail & Related papers (2024-04-09T15:01:51Z) - Streamlining the Selection Phase of Systematic Literature Reviews (SLRs) Using AI-Enabled GPT-4 Assistant API [0.0]
This study introduces a pioneering AI-based tool, configured specifically to streamline the efficiency of the article selection phase in Systematic Literature Reviews.
The tool successfully homogenizes the article selection process across a broad array of academic disciplines.
arXiv Detail & Related papers (2024-01-14T11:16:16Z) - Artificial intelligence to automate the systematic review of scientific
literature [0.0]
We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature.
We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
arXiv Detail & Related papers (2024-01-13T19:12:49Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - CitationIE: Leveraging the Citation Graph for Scientific Information
Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers.
We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.