Optimizing biomedical information retrieval with a keyword frequency-driven prompt enhancement strategy.

ABSTRACT:

Background

Mining the vast pool of biomedical literature to extract accurate responses and relevant references is challenging due to the domain's interdisciplinary nature, specialized jargon, and continuous evolution. Early natural language processing (NLP) approaches often led to incorrect answers as they failed to comprehend the nuances of natural language. However, transformer models have significantly advanced the field by enabling the creation of large language models (LLMs), enhancing question-answering (QA) tasks. Despite these advances, current LLM-based solutions for specialized domains like biology and biomedicine still struggle to generate up-to-date responses while avoiding "hallucination" or generating plausible but factually incorrect responses.

Results

Our wor

SUBMITTER: Aftab W 

PROVIDER: S-EPMC11351623 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature