Self Generated Response

Self-generated response research focuses on enabling language models (LLMs) to improve their outputs without extensive human supervision, primarily by leveraging the models' own evaluations of their generated text. Current approaches explore methods like self-play, where LLMs refine their responses by comparing self-generated alternatives, and self-alignment techniques that use internal knowledge for evaluating and improving factuality or adherence to specified principles. This research aims to reduce reliance on costly human feedback datasets and improve the overall quality and reliability of LLM outputs, impacting both the efficiency of LLM training and the trustworthiness of their applications.