A recent study published on arXiv investigates the impact of explanation-enhanced fine-tuning on language model classification performance. This approach involves supplementing labels with brief explanations during the fine-tuning process, aiming to improve the model's ability to classify more accurately. Researchers evaluated the conversational responses generated by the models using criteria such as naturalness, comprehensiveness, and relevance. The findings demonstrate that incorporating explanations alongside labels significantly enhances classification outcomes. This method, referred to as explanation-enhanced fine-tuning, shows systematic improvements compared to traditional fine-tuning techniques. The study's results are supported by multiple evaluations confirming the positive effect on model performance. These insights contribute to ongoing efforts to refine language model capabilities through more nuanced training strategies.