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A comprehensive survey of retrieval-augmented large language models for decision making in agriculture : unsolved problems and research opportunities

Vizniuk Artem, Diachenko Grigorii, Laktionov Ivan, Siwocha Agnieszka, Xiao Min, Smoląg Jacek

Journal of Artificial Intelligence and Soft Computing Research

2025

Vol. 15, No. 2

115--146

The breakthrough in developing large language models (LLMs) over the past few years has led to their widespread implementation in various areas of industry, business, and agriculture. The aim of this article is to critically analyse and generalise the known results and research directions on approaches to the development and utilisation of LLMs, with a particular focus on their functional characteristics when integrated into decision support systems (DSSs) for agricultural monitoring. The subject of the research is approaches to the development and integration of LLMs into DSSs for agrotechnical monitoring. The main scientific and applied results of the article are as follows: the world experience of using LLMs to improve agricultural processes has been analysed; a critical analysis of the functional characteristics of LLMs has been carried out, and the areas of application of their architectures have been identified; the necessity of focusing on retrieval-augmented generation (RAG) as an approach to solving one of the main limitations of LLMs, which is the limited knowledge base of training data, has been established; the characteristics and prospects of using LLMs for DSSs in agriculture have been analysed to highlight trustworthiness, explainability and bias reduction as priority areas of research; the potential socio-economic effect from the implementation of LLMs and RAG in the agricultural sector is substantiated.

Feasibility of using low-parameter local LLMS in answering questions from enterprise knowledge base

Badurowicz Marcin, Skulimowski Stanisław P., Laskowski Maciej

Applied Computer Science

2024

Vol. 20, no. 4

175--191

This paper evaluates the feasibility of deploying locally-run Large Language Models (LLMs) for retrieval-augmented question answering (RAG-QA) over internal knowledge bases in small and medium enterprises (SMEs), with a focus on Polish-language datasets. The study benchmarks eight popular open-source and source-available LLMs, including Google’s Gemma-9B and Speakleash’s Bielik-11B, assessing their performance across closed, open, and detailed question types, with metrics for language quality, factual accuracy, response stability, and processing efficiency. The results highlight that desktop-class LLMs, though limited in factual accuracy (with top scores of 45% and 43% for Gemma and Bielik, respectively), hold promise for early-stage enterprise implementations. Key findings include Bielik's superior performance on open-ended and detailed questions and Gemma's efficiency and reliability in closed-type queries. Distribution analyses revealed variability in model outputs, with Bielik and Gemma showing the most stable response distributions. This research underscores the potential of offline-capable LLMs as cost-effective tools for secure knowledge management in Polish SMEs.