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

• URL: http://arxiv.org/abs/2308.06610v1
• Date: Sat, 12 Aug 2023 16:56:55 GMT
• Title: Bio-SIEVE: Exploring Instruction Tuning Large Language Models for Systematic Review Automation
• Authors: Ambrose Robinson, William Thorne, Ben P. Wu, Abdullah Pandor, Munira Essat, Mark Stevenson, Xingyi Song
• Abstract summary: Large Language Models (LLMs) can support and be trained to perform literature screening for medical systematic reviews. Our best model, Bio-SIEVE, outperforms both ChatGPT and trained traditional approaches. We see Bio-SIEVE as an important step towards specialising LLMs for the biomedical systematic review process.
• Score: 6.452837513222072
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Medical systematic reviews can be very costly and resource intensive. We explore how Large Language Models (LLMs) can support and be trained to perform literature screening when provided with a detailed set of selection criteria. Specifically, we instruction tune LLaMA and Guanaco models to perform abstract screening for medical systematic reviews. Our best model, Bio-SIEVE, outperforms both ChatGPT and trained traditional approaches, and generalises better across medical domains. However, there remains the challenge of adapting the model to safety-first scenarios. We also explore the impact of multi-task training with Bio-SIEVE-Multi, including tasks such as PICO extraction and exclusion reasoning, but find that it is unable to match single-task Bio-SIEVE's performance. We see Bio-SIEVE as an important step towards specialising LLMs for the biomedical systematic review process and explore its future developmental opportunities. We release our models, code and a list of DOIs to reconstruct our dataset for reproducibility.

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