A team is fine-tuning a pre-trained language model using a dataset of high-quality instruction-response pairs. The training process aims to adjust the model's parameters to maximize the probability of it generating the exact target response for each given instruction. After training, the team observes that the model often produces responses that are factually correct but much shorter and less detailed than the high-quality examples in their dataset. What is the most likely reason for this behavior, given the training objective?

Comparing Training Objectives for Model Adaptation

Adapting a General Model for a Specialized Task