Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning
The episode discusses a paper titled "Stick to What You Know: A Study of Knowledge-Aligned Supervised Fine-Tuning." Hosts analyze how this method improves AI reliability by restricting training targets to the model's existing internal knowledge, effectively reducing factual hallucinations. The discussion concludes that prioritizing knowledge consistency is a major step toward building trustworthy and auditable AI systems.