Feb. 8, 2024, 5:46 a.m. | L Siddharth Jianxi Luo

cs.CL updates on arXiv.org arxiv.org

Despite significant popularity, Large-language Models (LLMs) require explicit, contextual facts to support domain-specific knowledge-intensive tasks in the design process. The applications built using LLMs should hence adopt Retrieval-Augmented Generation (RAG) to better suit the design process. In this article, we present a data-driven method to identify explicit facts from patent documents that provide standard descriptions of over 8 million artefacts. In our method, we train roBERTa Transformer-based sequence classification models using our dataset of 44,227 sentences and facts. Upon classifying …

applications article cs.cl cs.db cs.ir data data-driven design domain engineering engineering design facts graphs knowledge knowledge graphs language language models llms process rag retrieval retrieval-augmented support tasks

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