Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness

arXiv:2405.08151v1 Announce Type: new
Abstract: Large language models (LLM) have demonstrated remarkable capabilities in various biomedical natural language processing (NLP) tasks, leveraging the demonstration within the input context to adapt to new tasks. However, LLM is sensitive to the selection of demonstrations. To address the hallucination issue inherent in LLM, retrieval-augmented LLM (RAL) offers a solution by retrieving pertinent information from an established database. Nonetheless, existing research work lacks rigorous evaluation of the impact of retrieval-augmented large language models on …

abstract adapt application arxiv benchmarking biomedical capabilities context cs.cl hallucination however issue language language models language processing large language large language models llm natural natural language natural language processing nlp processing retrieval retrieval-augmented robustness self-awareness tasks type