All in One: An Empirical Study of GPT for Few-Shot Aspect-Based
  Sentiment Anlaysis
Baoxing Jiang
Aspect-Based Sentiment Analysis (ABSA) is an indispensable and highly
challenging task in natural language processing. Current efforts have focused
on specific sub-tasks, making it difficult to comprehensively cover all
sub-tasks within the ABSA domain. With the development of Generative
Pre-trained Transformers (GPTs), there came inspiration for a one-stop solution
to sentiment analysis. In this study, we used GPTs for all sub-tasks of
few-shot ABSA while defining a general learning paradigm for this application.
We propose the All in One (AiO) model, a simple yet effective two-stage model
for all ABSA sub-tasks. In the first stage, a specific backbone network learns
the semantic information of the review and generates heuristically enhanced
candidates. In the second stage, AiO leverages GPT contextual learning
capabilities to generate predictions. The study conducted comprehensive
comparative and ablation experiments on five benchmark datasets, and the
results show that AiO can effectively handle all ABSA sub-tasks, even with
few-shot data.