Decoder Only LLM

Decoder-only large language models (LLMs) are a rapidly evolving area of research focusing on improving the efficiency and capabilities of LLMs by eliminating the encoder component. Current research emphasizes enhancing context length through techniques like parallel decoding and efficient memory management, as well as mitigating issues like hallucinations and improving performance on tasks such as machine translation and question answering. These advancements are significant because they offer potential for more efficient and effective LLMs across diverse applications, including speech processing, computer vision, and code generation, while also pushing the boundaries of fundamental LLM architecture and training methodologies.