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.
Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models
AllSci Corp（AllSci公司）；Sunwater Capital（Sunwater资本）；Kellogg School of Management, Northwestern University（西北大学凯洛格管理学院）；Dept. of Computer Science, Northern Illinois University（北伊利诺伊大学计算机科学系）；Institute of Foundation Models, MBZUAI（基础模型研究所，MBZUAI）
Automating Exploratory Proteomics Research via Language Models
Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Frontis AI；National Center for Protein Sciences (Beijing)（国家蛋白质科学中心（北京））；Beijing Proteome Research Center（北京蛋白质组研究中心）；International Academy of Phronesis Medicine (Guangdong)（国际实践医学研究院（广东））
Automating quantum feature map design via large language models
Center for Quantum Information and Quantum Biology（量子信息与量子生物学中心）；Graduate School of Engineering Science（工程科学研究生院）；RIKEN Center for Quantum Computing（理化学研究所量子计算中心）
Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows
Department of Computer Science, Rensselaer Polytechnic Institute（里士满理工学院计算机科学系）；Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute（里士满理工学院机械、航空航天与核工程系）；Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；College of Education, Zhejiang Normal University（浙江师范大学教育学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
From Intention To Implementation: Automating Biomedical Research via LLMs
School of Informatics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen（信息学院、医学数据科学国家研究院、厦门大学、厦门）；School of Medicine, Xiamen University, Xiamen（医学院、厦门大学、厦门）；Microsoft Research Asia, Beijing（微软亚洲研究院、北京）；College of Computing, Georgia Institute of Technology, Atlanta（计算学院、佐治亚理工学院、亚特兰大）；Zhongguancun Academy, Beijing（中关村学院、北京）
Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University（欧布达大学应用信息学与应用数学博士学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺伊曼信息学学院）；Doctoral School of Innovation Management, Obuda University（欧布达大学创新管理博士学院）
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；Sinosoft Company Limited（中软公司）
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
The Lab for Internet Software Technologies Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所互联网软件技术实验室）；Graduate University of Chinese Academy of Sciences（中国科学院研究生院）；National ICT Australia, University of New South Wales（澳大利亚国家信息与通信技术研究院，新南威尔士大学）
[研究背景]：系统文献综述（Systematic Literature Review，SLR）已成为软件工程的一种重要研究方法。[Aim]本文提出了一种方法，利用经验演变的本体，以支持自动化的关键SLR活动。【方法】：首先，我们提出了一个本体，SLRONT，建立在SLR经验和最佳实践的基础上，以捕捉常见的术语和它们之间的关系在SLR过程中;第二，我们提出了一个扩展版本的SLRONT，COSONT和实例化它的知识和概念提取结构化摘要。案例研究说明了应用它来支持SLR步骤的细节。【结果】：结果表明，通过使用COSONT，我们得到了相同的结论，与纯粹的手工工作相比，但所涉及的工作量显着减少。[结论]利用本体论的方法可以有效地支持系统性文献综述的开展。
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
University of Warwick（华威大学）；Alan Turing Institute for Data Science and AI（艾伦·图灵数据科学与人工智能研究所）；Cranfield University（克兰菲尔德大学）；Government Outcomes Lab（政府成果实验室）；Blavatnik School of Government（布拉瓦尼克政府学院）；University of Oxford（牛津大学）
