PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews
  using Domain-specific Finetuned Large Language Models
Teo Susnjak
With the proliferation of open-sourced Large Language Models (LLMs) and
efficient finetuning techniques, we are on the cusp of the emergence of
numerous domain-specific LLMs that have been finetuned for expertise across
specialized fields and applications for which the current general-purpose LLMs
are unsuitable. In academia, this technology has the potential to revolutionize
the way we conduct systematic literature reviews (SLRs), access knowledge and
generate new insights. This paper proposes an AI-enabled methodological
framework that combines the power of LLMs with the rigorous reporting
guidelines of the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA). By finetuning LLMs on domain-specific academic papers
that have been selected as a result of a rigorous SLR process, the proposed
PRISMA-DFLLM (for Domain-specific Finetuned LLMs) reporting guidelines offer
the potential to achieve greater efficiency, reusability and scalability, while
also opening the potential for conducting incremental living systematic reviews
with the aid of LLMs. Additionally, the proposed approach for leveraging LLMs
for SLRs enables the dissemination of finetuned models, empowering researchers
to accelerate advancements and democratize cutting-edge research. This paper
presents the case for the feasibility of finetuned LLMs to support rigorous
SLRs and the technical requirements for realizing this. This work then proposes
the extended PRISMA-DFLLM checklist of reporting guidelines as well as the
advantages, challenges, and potential implications of implementing
PRISMA-DFLLM. Finally, a future research roadmap to develop this line of
AI-enabled SLRs is presented, paving the way for a new era of evidence
synthesis and knowledge discovery.