1. The assessment criteria for the performance of large language models is limited to accuracy metrics. Such a limited view does not necessarily provide a comprehensive representation of the performance of large language models in real-world applications.
2. The method exhibits dependence on similar examples from the training dataset. This raises potential concerns regarding the distribution consistency between the training and test datasets adopted in the study. An in-depth visualization and analysis of the data distributions might be beneficial to address such concerns.
3. The evaluative framework appears somewhat limited in scope. With considerations restricted to merely three Question-Answering tasks and two language models, there are reservations about the method's broader applicability. Its potential to generalize to other reasoning or generation tasks or more advanced models, such as vicunna or alpaca, remains a subject of inquiry.
3. The evaluative framework appears somewhat limited in scope. With considerations restricted to merely three Question-Answering tasks and two language models, there are reservations about the method's broader applicability. Its potential to generalize to other reasoning or generation tasks or more advanced models, such as vicunna or alpaca, remains a subject of inquiry.