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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.00807 |
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| _version_ | 1866913251471654912 |
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| author | Ni, Chunhe Wu, Jiang Wang, Hongbo Lu, Wenran Zhang, Chenwei |
| author_facet | Ni, Chunhe Wu, Jiang Wang, Hongbo Lu, Wenran Zhang, Chenwei |
| contents | Large Language Models (LLMs) are a class of generative AI models built using the Transformer network, capable of leveraging vast datasets to identify, summarize, translate, predict, and generate language. LLMs promise to revolutionize society, yet training these foundational models poses immense challenges. Semantic vector search within large language models is a potent technique that can significantly enhance search result accuracy and relevance. Unlike traditional keyword-based search methods, semantic search utilizes the meaning and context of words to grasp the intent behind queries and deliver more precise outcomes. Elasticsearch emerges as one of the most popular tools for implementing semantic search an exceptionally scalable and robust search engine designed for indexing and searching extensive datasets. In this article, we delve into the fundamentals of semantic search and explore how to harness Elasticsearch and Transformer models to bolster large language model processing paradigms. We gain a comprehensive understanding of semantic search principles and acquire practical skills for implementing semantic search in real-world model application scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models Ni, Chunhe Wu, Jiang Wang, Hongbo Lu, Wenran Zhang, Chenwei Information Retrieval Computation and Language Distributed, Parallel, and Cluster Computing Digital Libraries Large Language Models (LLMs) are a class of generative AI models built using the Transformer network, capable of leveraging vast datasets to identify, summarize, translate, predict, and generate language. LLMs promise to revolutionize society, yet training these foundational models poses immense challenges. Semantic vector search within large language models is a potent technique that can significantly enhance search result accuracy and relevance. Unlike traditional keyword-based search methods, semantic search utilizes the meaning and context of words to grasp the intent behind queries and deliver more precise outcomes. Elasticsearch emerges as one of the most popular tools for implementing semantic search an exceptionally scalable and robust search engine designed for indexing and searching extensive datasets. In this article, we delve into the fundamentals of semantic search and explore how to harness Elasticsearch and Transformer models to bolster large language model processing paradigms. We gain a comprehensive understanding of semantic search principles and acquire practical skills for implementing semantic search in real-world model application scenarios. |
| title | Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models |
| topic | Information Retrieval Computation and Language Distributed, Parallel, and Cluster Computing Digital Libraries |
| url | https://arxiv.org/abs/2403.00807 |