Streamlining the Selection Phase of Systematic Literature Reviews (SLRs) Using AI-Enabled GPT-4 Assistant API

• URL: http://arxiv.org/abs/2402.18582v1
• Date: Sun, 14 Jan 2024 11:16:16 GMT
• Title: Streamlining the Selection Phase of Systematic Literature Reviews (SLRs) Using AI-Enabled GPT-4 Assistant API
• Authors: Seyed Mohammad Ali Jafari,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The escalating volume of academic literature presents a formidable challenge in staying updated with the newest research developments. Addressing this, this study introduces a pioneering AI-based tool, configured specifically to streamline the efficiency of the article selection phase in Systematic Literature Reviews (SLRs). Utilizing the robust capabilities of OpenAI's GPT-4 Assistant API, the tool successfully homogenizes the article selection process across a broad array of academic disciplines. Implemented through a tripartite approach consisting of data preparation, AI-mediated article assessment, and structured result presentation, this tool significantly accelerates the time-consuming task of literature reviews. Importantly, this tool could be highly beneficial in fields such as management and economics, where the SLR process involves substantial human judgment. The adoption of a standard GPT model can substantially reduce potential biases and enhance the speed and precision of the SLR selection phase. This not only amplifies researcher productivity and accuracy but also denotes a considerable stride forward in the way academic research is conducted amidst the surging body of scholarly publications.

Related papers

• 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)
• From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions [60.733557487886635]
  This paper focuses on bridging the comprehension gap between Large Language Models and external tools. We propose a novel framework, DRAFT, aimed at Dynamically refining tool documentation. Extensive experiments on multiple datasets demonstrate that DRAFT's iterative, feedback-based refinement significantly ameliorates documentation quality.
  arXiv  Detail & Related papers  (2024-10-10T17:58:44Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• LLAssist: Simple Tools for Automating Literature Review Using Large Language Models [0.0]
  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.
  arXiv  Detail & Related papers  (2024-07-19T02:48:54Z)
• Artificial Intuition: Efficient Classification of Scientific Abstracts [42.299140272218274]
  Short scientific texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. To address this gap, we have developed a novel approach to generate and appropriately assign coarse domain-specific labels. We show that a Large Language Model (LLM) can provide metadata essential to the task, in a process akin to the augmentation of supplemental knowledge.
  arXiv  Detail & Related papers  (2024-07-08T16:34:47Z)
• Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning [0.9110413356918055]
  This research pioneers the use of fine-tuned Large Language Models (LLMs) to automate Systematic Literature Reviews ( SLRs) Our study employed the latest fine-tuning methodologies together with open-sourced LLMs, and demonstrated a practical and efficient approach to automating the final execution stages of an SLR process. The results maintained high fidelity in factual accuracy in LLM responses, and were validated through the replication of an existing PRISMA-conforming SLR.
  arXiv  Detail & Related papers  (2024-04-08T00:08:29Z)
• System for systematic literature review using multiple AI agents: Concept and an empirical evaluation [5.194208843843004]
  We introduce a novel multi-AI agent model designed to fully automate the process of conducting Systematic Literature Reviews. The model operates through a user-friendly interface where researchers input their topic. It generates a search string used to retrieve relevant academic papers. The model then autonomously summarizes the abstracts of these papers.
  arXiv  Detail & Related papers  (2024-03-13T10:27:52Z)
• 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)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• 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)
• Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review [77.34726150561087]
  This work presents a systematic literature review of state-of-the-art advances using transformer-based methods on electronic medical records (EMRs) in different NLP tasks.
  arXiv  Detail & Related papers  (2023-04-05T22:19:42Z)

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.