Search
26 articles for “Large Language Model (LLM)”
-
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
-
A Reviewed 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 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 of the study …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
-
Developing Techniques for Controlling Different Aspects of Text Generation Such as Tone and Contents
Abstract: Large Language Models (LLMs) have shown excellent text creation quality in Natural Language Processing (NLP). However, LLMs have to satisfy ever-more-complex standards in real-world applications. LLMs are supposed to meet specific user goals, like as mimicking specific writing styles or producing material with poetic richness, in addition to eliminating inaccurate or objectionable content. Controllable Text Generation (CTG) techniques were developed in response to these diverse demands. They guarantee that outputs …
Published in Recent Trends in Programming languages · Vol. 12, Issue 2, 2025 · pp. 34–39 Read article
-
Challenges, Risks, and Limitations of Vibe Coding in AI- Assisted Software Development
Abstract: Vibe coding a new way of programming driven by LLMs (Large Language Model).it helps developers to write code from natural language. Coders can use prompts to AI for there code generation. Ai assistant generate software directly threw the text. Now they are able to make tasks and understand the user requirements. This process make our development fast and easy for the developers. but vibe coding has some drawbacks sometime it …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 Read article
-
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
-
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