Transformer Based Large Language Model

Transformer-based large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text, with research focusing on improving efficiency, accuracy, and context handling. Current efforts concentrate on optimizing attention mechanisms (e.g., through low-rank approximation, dynamic layer operation, and random access strategies) and addressing limitations such as working memory capacity and length generalization. These advancements are significant because they enable more efficient deployment of LLMs across various applications, from question answering and text summarization to more complex tasks like multi-hop reasoning and clinical document analysis, while also furthering our understanding of both artificial and human intelligence.