May 15, 2024, 4:47 a.m. | Karthik Soman, Peter W Rose, John H Morris, Rabia E Akbas, Brett Smith, Braian Peetoom, Catalina Villouta-Reyes, Gabriel Cerono, Yongmei Shi, Angela R

cs.CL updates on

arXiv:2311.17330v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, requiring further domain expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful biomedical text rooted in established knowledge. Compared to …

abstract arxiv biomedical biomedicine challenges computational domain domains expertise face fine-tuning graph graph-based knowledge knowledge graph language language models large language large language models llms pre-training prompt rate replace retrieval robust solutions token training type

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