  Generative approaches have significantly influenced Aspect-Based Sentiment
Analysis (ABSA), garnering considerable attention. However, existing studies
often predict target text components monolithically, neglecting the benefits of
utilizing single elements for tuple prediction. In this paper, we introduce
Element to Tuple Prompting (E2TP), employing a two-step architecture. The
former step focuses on predicting single elements, while the latter step
completes the process by mapping these predicted elements to their
corresponding tuples. E2TP is inspired by human problem-solving, breaking down
tasks into manageable parts, using the first step's output as a guide in the
second step. Within this strategy, three types of paradigms, namely
E2TP($diet$), E2TP($f_1$), and E2TP($f_2$), are designed to facilitate the
training process. Beyond dataset-specific experiments, our paper addresses
cross-domain scenarios, demonstrating the effectiveness and generalizability of
the approach. By conducting a comprehensive analysis on various benchmarks, we
show that E2TP achieves new state-of-the-art results in nearly all cases.
