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

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 advanced fine-tuning methodologies on open sourced LLMs, applying textual data mining techniques to automate the knowledge discovery and synthesis phases of an SLR process, thus demonstrating a practical and efficient approach for extracting and analyzing high-quality information from large academic datasets. The results maintained high fidelity in factual accuracy in LLM responses, and were validated through the replication of an existing PRISMA-conforming SLR.…