Not All Demonstration Examples are Equally Beneficial: Reweighting
  Demonstration Examples for In-Context Learning
Zhe Yang Damai Dai Peiyi Wang Zhifang Sui
Large Language Models (LLMs) have recently gained the In-Context Learning
(ICL) ability with the models scaling up, allowing them to quickly adapt to
downstream tasks with only a few demonstration examples prepended in the input
sequence. Nonetheless, the current practice of ICL treats all demonstration
examples equally, which still warrants improvement, as the quality of examples
is usually uneven. In this paper, we investigate how to determine approximately
optimal weights for demonstration examples and how to apply them during ICL. To
assess the quality of weights in the absence of additional validation data, we
design a masked self-prediction (MSP) score that exhibits a strong correlation
with the final ICL performance. To expedite the weight-searching process, we
discretize the continuous weight space and adopt beam search. With
approximately optimal weights obtained, we further propose two strategies to
apply them to demonstrations at different model positions. Experimental results
on 8 text classification tasks show that our approach outperforms conventional
ICL by a large margin. Our code are publicly available at
https:github.com/Zhe-Young/WICL.