May 14, 2024, 4:49 a.m. | Haiyang Tang, Dongping Chen, Qingzhao Chu

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.06699v1 Announce Type: new
Abstract: With the rapid advancement of natural language processing technologies, generative artificial intelligence techniques, represented by large language models (LLMs), are gaining increasing prominence and demonstrating significant potential for applications in safety engineering. However, fundamental LLMs face constraints such as limited training data coverage and unreliable responses. This study develops a vector database from 117 explosion accident reports in China spanning 2013 to 2023, employing techniques such as corpus segmenting and vector embedding. By utilizing the …

abstract advancement applications artificial artificial intelligence arxiv assistant constraints coverage cs.ai cs.cl data database engineering face fundamental generative generative artificial intelligence however intelligence language language models language processing large language large language models llms natural natural language natural language processing processing question question answering safety safety engineering technologies training training data type vector vector database

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