  Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from
textual data about specific entities and their corresponding aspects through
various complementary subtasks. Several prior research has focused on
developing ad hoc designs of varying complexities for these subtasks. In this
paper, we present a generative framework extensible to any ABSA subtask. We
build upon the instruction tuned model proposed by Scaria et al. (2023), who
present an instruction-based model with task descriptions followed by
in-context examples on ABSA subtasks. We propose PFInstruct, an extension to
this instruction learning paradigm by appending an NLP-related task prefix to
the task description. This simple approach leads to improved performance across
all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the
ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average
of +5.43 F1-score across SemEval datasets. Furthermore, we explore the impact
of the prefix-enhanced prompt quality on the ABSA subtasks and find that even a
noisy prefix enhances model performance compared to the baseline. Our method
also achieves competitive results on a biomedical domain dataset (ERSA).
