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Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning
April 16, 2024, 4:50 a.m. | Teo Susnjak, Peter Hwang, Napoleon H. Reyes, Andre L. C. Barczak, Timothy R. McIntosh, Surangika Ranathunga
cs.CL updates on arXiv.org arxiv.org
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 …
abstract academic academic research arxiv automate cs.cl cs.dl cs.ir domain fine-tuning language language model language models large language large language model large language models literature llms model fine-tuning novel practical presenting research reviews study synthesis together type
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