  Aspect-based sentiment analysis (ABSA) identifies sentiment information
related to specific aspects and provides deeper market insights to businesses
and organizations. With the emergence of large language models (LMs), recent
studies have proposed using fixed examples for instruction tuning to
reformulate ABSA as a generation task. However, the performance is sensitive to
the selection of in-context examples; several retrieval methods are based on
surface similarity and are independent of the LM generative objective. This
study proposes an instruction learning method with retrieval-based example
ranking for ABSA tasks. For each target sample, an LM was applied as a scorer
to estimate the likelihood of the output given the input and a candidate
example as the prompt, and training examples were labeled as positive or
negative by ranking the scores. An alternating training schema is proposed to
train both the retriever and LM. Instructional prompts can be constructed using
high-quality examples. The LM is used for both scoring and inference, improving
the generation efficiency without incurring additional computational costs or
training difficulties. Extensive experiments on three ABSA subtasks verified
the effectiveness of the proposed method, demonstrating its superiority over
various strong baseline models. Code and data are released at
https://github.com/zgMin/IT-RER-ABSA.
