The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection

Researchers have discovered that using the highest-performing large language model as a teacher does not always produce the best results when training smaller student models. By analyzing answer selection strategies, the study indicates that student models perform better when they are trained on examples tailored to their specific learning needs rather than simply mimicking the most advanced model available. This shift suggests that optimizing the quality and relevance of training data is more effective than relying on the sheer capability of a teacher model.