Effective In-Context Example Selection through Data Compression
This addresses the lack of systematic research into in-context example selection for language models, offering a method to enhance performance in real-world applications.
The paper tackles the problem of selecting in-context examples for large language models by proposing a data compression approach, resulting in an average improvement of 5.90% across five real-world datasets using four models.
In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-stage method that can effectively choose relevant examples and retain sufficient information about the training dataset within the in-context examples. Our method shows a significant improvement of an average of 5.90% across five different real-world datasets using four language models.