Self Alignment

Self-alignment in large language models (LLMs) focuses on improving model behavior and aligning it with desired characteristics, such as adherence to cultural values or factual accuracy, without extensive human supervision. Current research explores various methods, including iterative self-enhancement paradigms, meta-rewarding techniques where the model judges its own responses, and resolving internal preference contradictions within the model. These advancements aim to reduce the reliance on costly human annotation and improve the reliability and safety of LLMs, impacting both the development of more robust AI systems and their practical applications across diverse fields.