LLM Based Baseline

LLM-based baselines serve as foundational models for improving various aspects of large language model (LLM) performance. Current research focuses on enhancing capabilities like code generation, decision-making in complex environments, and question answering by addressing limitations such as hallucinations and reliance on incomplete knowledge. This involves techniques such as reinforcement learning from AI feedback (RLAIF), automated guideline generation, and noise reduction strategies tailored to specific tasks. These advancements are significant because they improve the reliability and efficiency of LLMs across diverse applications, from smart home assistants to complex information retrieval.