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.

翻译：本研究开创性地运用微调后的大语言模型（LLMs）实现系统文献综述的自动化生成，在将人工智能融入学术研究方法论方面作出了重要且新颖的贡献。我们采用最新微调方法与开源大语言模型相结合，论证了一种实用高效的自动化方案，用于执行系统文献综述中涉及知识综合的最终阶段。实验结果表明，大语言模型在保持高事实准确性响应的同时，通过复现符合PRISMA标准的已有系统文献综述验证了有效性。本研究提出了缓解大语言模型幻觉的解决方案，并构建了追踪大语言模型响应信息来源的机制，从而证明了该方法能够满足学术研究的严格规范。最终发现证实，微调后的大语言模型具备简化文献综述中多项劳动密集型流程的潜力。鉴于该方法的普适性及其跨学科应用前景，本项基础研究还倡导更新PRISMA报告指南以纳入AI驱动流程，确保未来系统文献综述的方法透明性与可靠性。本研究拓展了人工智能增强工具在各类学术研究领域的影响力，为日益增长的学术文献背景下高效开展全面准确的文献综述设定了新标准。