LLM Based

Large language model (LLM)-based systems are rapidly advancing, aiming to improve efficiency and accuracy across diverse applications. Current research focuses on optimizing LLM performance through techniques like multi-agent systems, adaptive reward model selection (e.g., using multi-armed bandits), and integrating LLMs with symbolic methods for enhanced reasoning and planning capabilities. This work is significant because it addresses limitations of existing LLMs, such as inconsistency, hallucination, and computational cost, leading to more robust and reliable AI systems for various domains including healthcare, robotics, and software engineering.