Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning

• URL: http://arxiv.org/abs/2404.08680v1
• Date: Mon, 8 Apr 2024 00:08:29 GMT
• Title: Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning
• Authors: Teo Susnjak, Peter Hwang, Napoleon H. Reyes, Andre L. C. Barczak, Timothy R. McIntosh, Surangika Ranathunga,
• Abstract summary: 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.
• Score: 0.9110413356918055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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