A question-answering framework for automated abstract screening using large language models.

ABSTRACT:

Objective

This paper aims to address the challenges in abstract screening within systematic reviews (SR) by leveraging the zero-shot capabilities of large language models (LLMs).

Methods

We employ LLM to prioritize candidate studies by aligning abstracts with the selection criteria outlined in an SR protocol. Abstract screening was transformed into a novel question-answering (QA) framework, treating each selection criterion as a question addressed by LLM. The framework involves breaking down the selection criteria into multiple questions, properly prompting LLM to answer each question, scoring and re-ranking each answer, and combining the responses to make nuanced inclusion or exclusion decisions.

Results and discussion

Large-scale validation was performed on the benc

SUBMITTER: Akinseloyin O 

PROVIDER: S-EPMC11339526 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature