LLAssist: Simple Tools for Automating Literature Review Using Large Language Models
- URL: http://arxiv.org/abs/2407.13993v1
- Date: Fri, 19 Jul 2024 02:48:54 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
- 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) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Artificial Intelligence for Literature Reviews: Opportunities and
Challenges [0.0]
This manuscript presents a comprehensive review of the use of Artificial Intelligence in Systematic Literature Reviews.
A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic.
We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features.
arXiv Detail & Related papers (2024-02-13T16:05:51Z) - 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) - AI Literature Review Suite [0.0]
I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review.
This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files.
The suite also features integrated programs for organization, interaction and query, and literature review summaries.
arXiv Detail & Related papers (2023-07-27T17:30:31Z) - Algorithmic Ghost in the Research Shell: Large Language Models and
Academic Knowledge Creation in Management Research [0.0]
The paper looks at the role of large language models in academic knowledge creation.
This includes writing, editing, reviewing, dataset creation and curation.
arXiv Detail & Related papers (2023-03-10T14:25:29Z) - 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.