To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering
<|reference_start|>To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering: Medical open-domain question answering demands substantial access to specialized knowledge. Recent efforts have sought to decouple knowledge from model parameters, counteracting architectural scaling and allowing for training on common low-resource hardware. The retrieve-then-read paradigm has become ubiquitous, with model predictions grounded on relevant knowledge pieces from external repositories such as PubMed, textbooks, and UMLS. An alternative path, still under-explored but made possible by the advent of domain-specific large language models, entails constructing artificial contexts through prompting. As a result, "to generate or to retrieve" is the modern equivalent of Hamlet's dilemma. This paper presents MedGENIE, the first generate-then-read framework for multiple-choice question answering in medicine. We conduct extensive experiments on MedQA-USMLE, MedMCQA, and MMLU, incorporating a practical perspective by assuming a maximum of 24GB VRAM. MedGENIE sets a new state-of-the-art in the open-book setting of each testbed, allowing a small-scale reader to outcompete zero-shot closed-book 175B baselines while using up to 706$\times$ fewer parameters. Our findings reveal that generated passages are more effective than retrieved ones in attaining higher accuracy.<|reference_end|>
@article{frisoni2024to,
    title={To Generate or to Retrieve? On the Effectiveness of Artificial Contexts
  for Medical Open-Domain Question Answering},
    author={Giacomo Frisoni, Alessio Cocchieri, Alex Presepi, Gianluca Moro,
  Zaiqiao Meng},
    journal={arXiv preprint arXiv:2403.01924},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01924},
    primaryClass={cs.CL cs.AI}
}