This research pioneers the use of fine-tuned Large Language Models (LLMs) to automate Systematic Literature Reviews (SLRs), presenting a significant and novel contribution in integrating AI to enhance academic research methodologies. 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 that involves knowledge synthesis. The results maintained high fidelity in factual accuracy in LLM responses, and were validated through the replication of an existing PRISMA-conforming SLR. Our research proposed solutions for mitigating LLM hallucination and proposed mechanisms for tracking LLM responses to their sources of information, thus demonstrating how this approach can meet the rigorous demands of scholarly research. The findings ultimately confirmed the potential of fine-tuned LLMs in streamlining various labor-intensive processes of conducting literature reviews. Given the potential of this approach and its applicability across all research domains, this foundational study also advocated for updating PRISMA reporting guidelines to incorporate AI-driven processes, ensuring methodological transparency and reliability in future SLRs. This study broadens the appeal of AI-enhanced tools across various academic and research fields, setting a new standard for conducting comprehensive and accurate literature reviews with more efficiency in the face of ever-increasing volumes of academic studies.
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China（湍流与复杂系统国家重点实验室，工程学院，北京大学，北京100871，中国）；HEDPS-CAPT, Peking University, Beijing 100871, China（HEDPS-CAPT，北京大学，北京100871，中国）
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
University of Chicago（芝加哥大学）；Peking University（北京大学）；University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；UNC-Chapel Hill（北卡罗来纳大学教堂山分校）
Automating Steering for Safe Multimodal Large Language Models
Zhejiang University（浙江大学）；Zhejiang University - Ant Group Joint Lab of Knowledge Graph（浙江大学-蚂蚁集团知识图谱联合实验室）；National University of Singapore, NUS-NCS Joint Lab（新加坡国立大学NUS-NCS联合实验室）
TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Baidu Inc（百度公司）
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；Johns Hopkins University（约翰霍普金斯大学）；Shanghai Jiao Tong University（上海交通大学）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology（香港科技大学）
