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

arXiv: https://arxiv.org/abs/2308.03107 · HTML: https://mlsanity.com/paper/2308.03107/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [LLM-Pruner: On the Structural Pruning of Large Language Models](https://mlsanity.com/paper/2305.11627/) (2023)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [DOM-LM: Learning Generalizable Representations for HTML Documents](https://mlsanity.com/paper/2201.10608/) (2022)

## Built on by (most cited first, 16 of 16)

- [IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators](https://mlsanity.com/paper/2402.00345/) (2024)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain](https://mlsanity.com/paper/2402.14871/) (2024)
- [Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits](https://mlsanity.com/paper/2403.14791/) (2024)
- [AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application](https://mlsanity.com/paper/2503.04788/) (2025)
- [Multi-Document Financial Question Answering using LLMs](https://mlsanity.com/paper/2411.07264/) (2024)
- [Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and Tracking](https://mlsanity.com/paper/2503.16538/) (2025)
- [Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems](https://mlsanity.com/paper/2409.14682/) (2024)
- [AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models](https://mlsanity.com/paper/2507.21773/) (2025)
- [Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling](https://mlsanity.com/paper/2508.00843/) (2025)
- [PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification](https://mlsanity.com/paper/2404.19744/) (2024)
- [From General to Specialized: The Need for Foundational Models in Agriculture](https://mlsanity.com/paper/2507.05390/) (2025)
- [ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models](https://mlsanity.com/paper/2507.05568/) (2025)
- [Extracting chemical food safety hazards from the scientific literature automatically using large language models](https://mlsanity.com/paper/2405.15787/) (2024)
- [AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models](https://mlsanity.com/paper/2601.08283/) (2026)
- [AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval](https://mlsanity.com/paper/2604.16353/) (2026)

## Abstract (arXiv)

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.