Large Language Models (LLMs) have demonstrated their In-Context Learning
(ICL) capabilities which provides an opportunity to perform few shot learning
without any gradient update. Despite its multiple benefits, ICL generalization
performance is sensitive to the selected demonstrations. Selecting effective
demonstrations for ICL is still an open research challenge. To address this
challenge, we propose a demonstration selection method called InfICL which
analyzes influences of training samples through influence functions.
Identifying highly influential training samples can potentially aid in
uplifting the ICL generalization performance. To limit the running cost of
InfICL, we only employ the LLM to generate sample embeddings, and don't perform
any costly fine tuning. We perform empirical study on multiple real-world
datasets and show merits of our InfICL against state-of-the-art baselines.

通过使用影响函数分析训练样本的影响力，我们提出了一种名为 InfICL 的演示选择方法，用于选择对 In-Context Learning（ICL）有高影响力的训练样本，从而提高 ICL 的泛化性能，并在多个实际数据集上展示了 InfICL 相对于现有基准方法的优点。