Response Quality

Response quality in large language models (LLMs) focuses on improving the accuracy, relevance, and overall helpfulness of model outputs, addressing issues like factual errors and biases. Current research emphasizes improving training data through techniques like multi-agent cooperation and uncertainty-aware reward models, as well as refining inference strategies such as query routing and response reranking to optimize for both cost and quality. These advancements aim to enhance the reliability and trustworthiness of LLMs across various applications, particularly in sensitive domains like healthcare, while also improving efficiency.