A Comprehensive Overview of Large Language Models
Abstract
Large Language Models (LLMs) have emerged as a transformative force in natural language processing (NLP), enabling machines to understand, generate, and interact using human language with remarkable fluency. Built on deep learning architectures like the Transformer, models such as GPT, BERT, and LLaMA are trained on massive text corpora, allowing them to capture complex linguistic patterns, perform reasoning, and generalize across a wide range of tasks. These models have found applications in numerous domains including customer service, education, programming, healthcare, and legal analysis. They support tasks such as summarization, translation, content generation, and conversational AI, and are increasingly being integrated into real-world tools like search engines and virtual assistants. However, LLMs come with challenges. Their outputs may reflect biases present in training data and can sometimes generate factually incorrect or harmful content. The computational resources required for training and deployment also raise concerns regarding energy consumption and accessibility. Researchers are actively addressing these issues through model optimization, ethical alignment techniques such as reinforcement learning with human feedback (RLHF), and development of open-source alternatives. As LLMs evolve, future directions include multimodal integration, enhanced reasoning capabilities, and improved transparency. Interdisciplinary collaboration is essential to ensure responsible use of these powerful models. In summary, LLMs are reshaping the AI landscape by enabling more natural and intelligent interactions, but their development and deployment must be guided by ethical considerations and inclusive practices.
Details
| Title: | A Comprehensive Overview of Large Language Models |
| Subjects: | Engineering |
| More Details: | View PDF |
| Report Article: | Report |