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81 articles for “Large Language Model”
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Security Operations Centers (SOCs) must interpret large volumes of alerts, logs, endpoint events, identity signals, threat intelligence, and cloud activity while responding quickly to incidents. Conventional automation reduces repetitive work, but it often depends on fixed rules and separate tools that still require analysts to assemble context manually. This review presents a practical framework for AI (Artificial Intelligence) agent-based security copilots that combine large language models, machine learning, retrieval-augmented generation (RAG), security analytics, and controlled tools used to support SOC teams. The proposed design uses specialized agents for alert triage, investigation, threat intelligence, detection engineering, response planning, and reporting, coordinated by an orchestration layer. Agents can collect evidence, summarize events, map behaviors to attack techniques, suggest hypotheses, and prepare response actions, but high-impact changes remain subject to human approval. The framework also emphasizes trustworthy operation through evidence citation, least-privilege access, policy gates, audit logs, prompt-injection resistance, privacy controls, and continuous evaluation. Electrical and electronics relevance is included through monitoring of Internet of Things devices, industrial control systems, programmable logic controllers (PLC), edge gateways, sensors, firmware, and operational technology (OT) networks. Recent research and industry practice indicate that large language models and autonomous agents can improve security workflow automation, while important limitations remain around hallucination, data leakage, adversarial manipulation, excessive agency, and model drift. The paper therefore treats the security copilot as an analyst augmentation layer rather than an unsupervised replacement for professional judgment.
Abstract: Security Operations Centers (SOCs) must interpret large volumes of alerts, logs, endpoint events, identity signals, threat intelligence, and cloud activity while responding quickly to incidents. Conventional automation reduces repetitive work, but it often depends on fixed rules and separate tools that still require analysts to assemble context manually. This review presents a practical framework for AI (Artificial Intelligence) agent-based security copilots that combine large language models, machine learning, retrieval-augmented generation …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 3, 2026 Read article
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Semantic Similarity Framework for Automatic Hallucination Detection in Large Language Models
Abstract: Large Language Models can generate fluent, contextually appropriate text across a range of NLP tasks, but they frequently produce outputs that are factually wrong while sounding confident and plausible. This problem, referred to as hallucination, poses serious risks in domains where accuracy matters. We propose a post-processing framework that detects hallucinated responses by comparing them against verified reference text using sentence embeddings. The system computes cosine similarity between the response …
Published in Emerging Trends in Languages · Vol. 3, Issue 2, 2026 · pp. 16–22 Read article
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Confident but Wrong: A Lifecycle Analysis of Hallucination in Large Language Models Understanding AI's Confidence Problem
Abstract: A Large Language Model (LLM) is likely to produce sentences that are fluent and confident in many instances, but completely wrong. In many cases a Large Language Model (LLM) will produce a fluent and confident sentence that is completely incorrect. This review tries to analyze this phenomenon using the most recent literature regarding natural language generation, computational learning theory and benchmark experiments. The argument is that it is not a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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MentaLLaMA: Advancing Mental Health Insights with Instruction-Finetuned Large Language Models
Abstract: The growing prevalence of mental health challenges in contemporary society has highlighted the urgent need for advanced, interpretable, and reliable artificial intelligence solutions that can support mental health assessment and intervention. In response to this need, this research introduces a novel collection of open-source, instruction-tuned large language models (LLMs) specifically designed to facilitate transparent and accurate mental health evaluations. Leveraging a newly developed dataset, which integrates multiple tasks and diverse …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 08–15 Read article
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Reward-Aligned Reinforcement Learning from Human Feedback for Emotion-Sensitive Large Language Model Therapists: Balancing Empathetic Engagement, Boundary Safety, and Clinical Accountability
Abstract: The deployment of large language models (LLMs) in mental health therapy contexts introduces a critical alignment challenge: these systems must simultaneously cultivate genuine empathic rapport, observe clinically grounded safety boundaries, and remain auditable under institutional accountability frameworks. Existing reinforcement learning from human feedback (RLHF) pipelines optimize for a scalar reward signal that is demonstrably insufficient for the multi-objective, temporally extended nature of therapeutic conversation. This paper presents RA-RLHF-T (Reward-Aligned RLHF …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 2, 2026 Read article
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Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots
Abstract: This study presents a detailed framework for developing personalized chatbots that utilize large language models (LLMs) to process and extract information from extensive documents while effectively responding to user inquiries. The proposed system is designed to mitigate information overload by employing advanced natural language processing techniques, leveraging technologies such as OpenAI, LangChain, and Streamlit. By integrating these tools, the framework enhances knowledge retrieval, simplifies document comprehension, and improves overall productivity. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 88–93 Read article
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Text-to-SQL Systems: A Comprehensive Study of Evolution, Architectural Advancements, Challenges, and the Role of Large Language Models in Intelligent Query Generation
Abstract: Interacting with relational databases traditionally requires knowledge of Structured Query Language (SQL), which creates a significant barrier for users who do not have technical expertise. To address this challenge, Text-to-SQL systems have emerged as an important area of research, enabling users to express their information needs in natural language while automatically generating executable SQL queries. These systems aim to make database access more intuitive, efficient, and accessible across a wide …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 2, 2026 · pp. 10–19 Read article
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Generative Artificial Intelligence with Emphasis on Large Language Models: Review and Current Trends
Abstract: Generative Artificial Intelligence deals with AI systems that generate new content, such as text, and images. It accomplishes this by using data patterns of texts and images that already exist. Generative AI began an era of major advancement in AI, producing more refined and human-like results. Large Language Models, LLMs, is a part of Generative AI with applications in Natural Language Processing such as text generation, translation, summarization, sentiment detection …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 40–46 Read article
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A Review Study on CPU-Optimized Parameter-Efficient Fine-Tuning for Large Language Models to Increase Accuracy Using LoRA
Abstract: The fast proliferation of large language models (LLMs) has increased the need to optimize the process of fine-tuning, but the existing workflows that require a graphics processing unit (GPU) are still expensive, intensive, and unavailable to most researchers. This paper is driven by the desire to have a more cost-efficient and democratized version by examining a CPU-efficient implementation of parameter-efficient fine-tuning (PEFT) based on low-rank adaptation (LoRA). The major purpose …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 32–38 Read article
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Methodology for Evaluating Code Synthesis in Large Language Models: ChatGPT and Copilot: A Review
Abstract: The authors introduce a comprehensive framework to assess the code-generation capabilities of large language models, focusing on ChatGPT and Copilot through a benchmark suite of 25 program synthesis tasks. Their main goal was to show why making proper comparisons is important, they did not focus on choosing the newest models, since they keep changing frequently. The critique examines how the methodology addresses both functional and non-functional aspects of code. In …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 01–07 Read article
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Agentic AI-Driven Negative Emission Optimization: Large Language Model Orchestration and Quantum Annealing for Real-Time Carbon Capture Policy Simulation Beyond 1.5°C Thresholds
Abstract: The convergence of agentic artificial intelligence, large language model (LLM) orchestration, and quantum annealing presents a transformative paradigm for climate policy computation. This paper introduces Nega Optima a multi-agent computational architecture that integrates LLM-driven policy parsing, quantum annealing-based combinatorial optimization, and real-time integrated assessment model (IAM) feedback loops to simulate negative emission technology (NET) deployment strategies in a post-1.5°C overshoot scenario. Existing carbon capture optimization frameworks suffer from three systemic …
Published in International Journal of Climate Conditions · Vol. 3, Issue 2, 2026 · pp. 7–14 Read article
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Smart Service Solutions AI: AI-driven Multimodal Search for Home Appliance Support
Abstract: The global home appliance services market is projected to reach USD 1,203.11 billion by 2032, driven by smart household appliances. Essential services like installations, maintenance, and repairs ensure optimal performance and longevity, leading to higher customer satisfaction and loyalty. The service station partner ecosystem is crucial for ongoing support and profitability. However, challenges such as identifying the right manuals, finding relevant parts, and providing accurate instructions must be addressed to …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 27–43 Read article
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Unmasking Hallucinations in Large Language Models Using Analysis of the LLAMA 2 Model and RAG Intervention
Abstract: The study describes the creation of a chatbot for financial trading called "TradeBot" and how it uses Retrieval Augmented Generation (RAG) to overcome the problem of producing false or unverifiable information, sometimes known as hallucinations. RAG allows the chatbot to refer to an external data source in addition to its taught knowledge, which increases the accuracy of its responses. The NCFM (NSE's Certification in Financial Markets) book was integrated as …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 76–86 Read article
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Evaluating Open-Source Large Language Models for Educational Applications
Abstract: Mobile healthcare technologies generate sensitive information through electronic health records, smartphones, wearable sensors, medical imaging systems, and remote monitoring platforms. These distributed data sources can improve disease prediction, continuous monitoring, clinical decision support, and personalized healthcare, but centralized collection creates substantial privacy, security, interoperability, and data-governance concerns. Federated learning (FL) provides a distributed machine learning paradigm in which participating healthcare institutions and personal devices collaboratively train models while retaining raw …
Published in Journal of Open Source Developments · Vol. 13, Issue 2, 2026 · pp. 10–21 Read article
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Insurance Chatbot for Customer Support Using LLM and Generative AI
Abstract: The Insurance Chatbot for Customer Support is an AI inspired solution designed to streamline customer interactions within the insurance industry. The main purpose of Large Language Models and Generative AI in the chatbot enhances accessibility to policy details, facilitates seamless policy enrollment and enables real time claims tracking. Its ability to process natural language queries ensures accurate and intuitive responses, significantly reducing customer confusion and operational inefficiencies. This chatbot addresses …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 2, 2026 · pp. 41–47 Read article
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Continuous Learning in Language Models: A Survey of Streaming Data Processing Techniques
Abstract: The integration of continual learning with Large Language Models (LLMs) and Natural Language Processing (NLP) represents a transformative step toward creating adaptive, intelligent systems capable of functioning effectively in ever-changing environments. Traditional LLMs are typically trained on large, pre-collected datasets, which limits their ability to evolve as new information emerges. Continual learning, in contrast, enables models to acquire new knowledge incrementally without the need for complete retraining, thereby supporting long-term …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 23–34 Read article
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AI Chatbot for Expressing Visual Content
Abstract: Recently, the artificial intelligence (AI) chatbot for expressing visual content has shown remarkable multi-modal capabilities. It can recognize funny features in photos and create webpages straight from handwritten text. These characteristics are uncommon in earlier vision language models. We think the use of a more sophisticated large language model (LLM) is the main factor behind vision verbalizer's superior multi-modal generating capabilities. We introduce vision verbalizer, which employs a single projection …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 2, 2024 · pp. 11–19 Read article
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AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models
Abstract: As the field of artificial intelligence (AI) continues to evolve at a fast pace, and specifically with the rise of large language models (LLMs), the software design, implementation, testing, and maintenance landscape has undergone unprecedented transformation. Although programming languages are still the main language for writing computational logic, the way developers interact with programming languages is now increasingly mediated by AI-based tools. Current AI coding systems can help with code …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 43–50 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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Mindwell: A Psychological Guide for Well-being
Abstract: Mental health is a crucial aspect of overall well-being, yet access to professional therapy remains a significant challenge for many individuals due to various barriers, including cost, availability, and stigma. This research aims to develop an accessible and effective mental health therapy chatbot, named Mindwell Psychology, leveraging the power of large language models (LLMs) and state-of-the-art natural language processing techniques. The primary objective of this study is to create a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 63–77 Read article