While high-throughput sequencing technologies have dramatically accelerated genomic data generation, the manual processes required for dataset annotation and metadata creation impede the efficient discovery and publication of these resources across disparate public repositories. Large Language Models (LLMs) have the potential to streamline dataset profiling and discovery. However, their current limitations in generalizing across specialized knowledge domains, particularly in fields such as biomedical genomics, prevent them from fully realizing this potential. This paper presents Pre-Meta, an LLM-agnostic and domain-independent data annotation pipeline with an enriched retrieval procedure that leverages related priors-such as pre-generated metadata tags and ontologies-as auxiliary information to improve the accuracy of automated metadata generation. Validated using five selected metadata fields sampled across 1500 papers, the Pre-Meta assisted annotation experiment-without finetuning and prompt optimization-demonstrates a systemic improvement in the annotation task: shown through a 23%, 72%, and 75% accuracy gain from conventional RAG adoptions of GPT-4o mini, Llama 8B, and Mistral 7B respectively. The code, data access, and scripts are available at: https://github.com/SINTEF-SE/LLMDap.
Tinn et al. (Thu,) studied this question.