Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data

• URL: http://arxiv.org/abs/2308.03107v1
• Date: Sun, 6 Aug 2023 13:18:38 GMT
• Title: Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data
• Authors: Ruoling Peng, Kang Liu, Po Yang, Zhipeng Yuan, Shunbao Li
• Abstract summary: We explore using domain-agnostic general pre-trained large language model(LLM) to extract structured data from agricultural documents with minimal or no human intervention. In comparison to existing methods, our approach achieves consistently better accuracy in the benchmark while maintaining efficiency.
• Score: 5.573704309892796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pest identification is a crucial aspect of pest control in agriculture. However, most farmers are not capable of accurately identifying pests in the field, and there is a limited number of structured data sources available for rapid querying. In this work, we explored using domain-agnostic general pre-trained large language model(LLM) to extract structured data from agricultural documents with minimal or no human intervention. We propose a methodology that involves text retrieval and filtering using embedding-based retrieval, followed by LLM question-answering to automatically extract entities and attributes from the documents, and transform them into structured data. In comparison to existing methods, our approach achieves consistently better accuracy in the benchmark while maintaining efficiency.

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