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Medical systematic reviews can be very costly and resource intensive. Weexplore how Large Language Models (LLMs) can support and be trained to performliterature screening when provided with a detailed set of selection criteria.Specifically, we instruction tune LLaMA and Guanaco models to perform abstractscreening for medical systematic reviews. Our best model, Bio-SIEVE,outperforms both ChatGPT and trained traditional approaches, and generalisesbetter across medical domains. However, there remains the challenge of adaptingthe model to safety-first scenarios. We also explore the impact of multi-tasktraining with Bio-SIEVE-Multi, including tasks such as PICO extraction andexclusion reasoning, but find that it is unable to match single-taskBio-SIEVE's performance. We see Bio-SIEVE as an important step towardsspecialising LLMs for the biomedical systematic review process and explore itsfuture developmental opportunities. We release our models, code and a list ofDOIs to reconstruct our dataset for reproducibility.

Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation