This paper conducts an analysis of four transformer-based language models in the context of biomedical information retrieval, utilizing a dataset of 480,000 research papers. The study reveals that models with less than 10B parameters, when fine-tuned on domain-specific data, achieve superior performance in terms of accuracy and relevance compared to larger models, translating to a significant average performance increase of 50%. Interestingly, while larger models excel on broader prompts, their specificity lacks the same precision as their smaller counterparts in niche inquiries.
Our findings suggest that smaller models, with <10B parameters and fine-tuned on domain-specific datasets, tend to outperform larger language models on highly specific questions in terms of accuracy, relevancy, and interpretability by a significant margin (+50% on average).
#natural-language-processing #biomedical-information-retrieval #model-performance #fine-tuning #machine-learning
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