Articles

Large language models (LLMs) have paved a way for geographic information system (GIS) that can solve spatial problems with minimal human intervention. However, current commercial LLM-based GIS solutions pose many limitations for researchers, such as proprietary APIs, high operational costs, and internet connectivity requirements, making them inaccessible in resource-constrained environments. To overcome this, this paper introduced the LLM-Geo framework with the DS-GeoAI platform, integrating the DeepSeek-Coder model (the open-source, lightweight version deepseek-coder-1.3b-base) running directly on Google Colab. This approach eliminates API dependence, thus reducing deployment costs, and ensures data independence and sovereignty. Despite having only 1.3 billion parameters, DeepSeek-Coder proved to be highly effective: generating accurate Python code for complex spatial analysis, achieving a success rate comparable to commercial solutions. After an automated debugging step, the system achieved 90% accuracy across three case studies. With its strong error- handling capabilities and intelligent sample data generation, DS-GeoAI proves highly adaptable to real-world challenges. Quantitative results showed a cost reduction of up to 99% compared to API-based solutions, while expanding access to advanced geo-AI technology for organizations with limited resources.

This research paper explores the use of large language model Meta AI 3 (LLaMA 3) for automating the resume screening process. Traditional resume screening methods that rely on keyword searching and human review can be inefficient, biased, and fail to identify qualified candidates. LLaMA 3, trained on large-scale text datasets, has the potential to accurately analyze resumes by understanding context and semantic details beyond simple keyword matching.The study presents a system that converts resume PDFs to text, inputs the text along with the job description into the LLaMA 3 model, and generates a ranked list of candidates with reasoning for their job fit. This discusses the data preparation, model setup, and performance evaluation of this system. Results show LLaMA 3 can rapidly process batches of resumes while reducing human bias in the screening process. The system aims to streamline hiring by automating the initial resume screening stage to surface top candidates for further in-depth evaluation. Key benefits include improved accuracy in identifying relevant skills, reduced bias compared to human screeners, and significant time savings for recruiters. The paper also examines ethical considerations around using AI for hiring decisions. Overall, this work demonstrates the promising application of large language models (LLMs) like LLaMA 3 to transform and enhance resume screening practices.

The rapid advancements in artificial intelligence (AI) and robotics have paved the way for innovative autonomous systems capable of performing complex tasks. This project integrates robotics with Large Language Models (LLMs) to develop an intelligent, versatile and user-friendly robotic system. The robot is designed to interpret structured commands, make real-time decisions, and navigate autonomously in dynamic environments, addressing key challenges faced by traditional autonomous systems. Central to the system is a Raspberry Pi 4, which serves as the main processing unit, integrating components such as a webcam for visual data capture, an L298N motor driver for motor control, and a Bluetooth speaker for real-time feedback. The LLM API enables the robot to process natural language commands, providing context-aware task execution and adaptability to changing scenarios. Testing has demonstrated the system’s ability to perform autonomous navigation, detect obstacles, and execute tasks effectively. This research offers a foundation for various industries, including logistics, healthcare, education, and hazardous environment operations. By incorporating LLMs the robot overcomes limitations of traditional rule-based systems, enhancing dynamic decision-making and user interaction. With its modular design and scalability, it bridges the gap between human-like intelligence and mechanical precision, setting the stage for future advancements in AI-driven robotics.

This study aims to examine teachers’ perceptions of the effectiveness of large language models (LLM) in supporting data-driven decision-making in educational contexts. Specifically, the study explores the influence of technological pedagogical knowledge, technological content knowledge, and technological pedagogical content knowledge on teachers’ utilization of LLMs for informed decision-making. Additionally, it investigates the moderating role of ethical considerations in the use of LLMs. A survey-based methodology was employed to collect data from university teachers in Chengdu, Sichuan, China, resulting in a sample of 319 respondents, which was analyzed using Smart PLS 4. The findings indicate that technological pedagogical knowledge, technological content knowledge, and technological pedagogical content knowledge for LLM use significantly impact data-driven decision-making in teaching. Moreover, ethical considerations were found to significantly moderate the relationship between these knowledge domains and data-driven decision-making. This study contributes novel insights by addressing the evaluation and application of LLM effectiveness from teachers’ perspectives, ultimately fostering the advancement of quality education.

This paper presents and discusses a framework that leverages large-scale language models (LLMs) for data enrichment and continuous monitoring emphasizing its essential role in optimizing the performance of deployed models. It introduces a comprehensive large language model operations (LLMOps) methodology based on continuous monitoring and continuous improvement of the data, the primary determinant of the model, in order to optimize the prediction of a given phenomenon. To this end, first we examine the use of real-time web scraping using tools such as Kafka and Spark Streaming for data acquisition and processing. In addition, we explore the integration of LLMOps for complete lifecycle management of machine learning models. Focusing on continuous monitoring and improvement, we highlight the importance of this approach for ensuring optimal performance of deployed models based on data and machine learning (ML) model monitoring. We also illustrate this methodology through a case study based on real data from several real estate listing sites, demonstrating how MLflow can be integrated into an LLMOps pipeline to guarantee complete development traceability, proactive detection of performance degradations and effective model lifecycle management.