Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level
sentiment analysis. Current ASQP datasets are characterized by their small size
and low quadruple density, which hinders technical development. To expand
capacity, we construct two large Chinese ASQP datasets crawled from multiple
online platforms. The datasets hold several significant characteristics: larger
size (each with 10,000+ samples) and rich aspect categories, more words per
sentence, and higher density than existing ASQP datasets. Moreover, we are the
first to evaluate the performance of Generative Pre-trained Transformer (GPT)
series models on ASQP and exhibit potential issues. The experiments with
state-of-the-art ASQP baselines underscore the need to explore additional
techniques to address ASQP, as well as the importance of further investigation
into methods to improve the performance of GPTs.

通过构建两个大型的中文 aspect sentiment quad prediction（ASQP）数据集，我们评估了 GPT 系列模型在 ASQP 上的性能，并展示了潜在的问题，同时强调了需要探索额外技术以解决 ASQP 以及改善 GPT 性能的重要性。