  The DimABSA task requires fine-grained sentiment intensity prediction for
restaurant reviews, including scores for Valence and Arousal dimensions for
each Aspect Term. In this study, we propose a Coarse-to-Fine In-context
Learning(CFICL) method based on the Baichuan2-7B model for the DimABSA task in
the SIGHAN 2024 workshop. Our method improves prediction accuracy through a
two-stage optimization process. In the first stage, we use fixed in-context
examples and prompt templates to enhance the model's sentiment recognition
capability and provide initial predictions for the test data. In the second
stage, we encode the Opinion field using BERT and select the most similar
training data as new in-context examples based on similarity. These examples
include the Opinion field and its scores, as well as related opinion words and
their average scores. By filtering for sentiment polarity, we ensure that the
examples are consistent with the test data. Our method significantly improves
prediction accuracy and consistency by effectively utilizing training data and
optimizing in-context examples, as validated by experimental results.
