In-Context Learning (ICL) is suffering from unsatisfactory performance and under-calibration due to high prior bias and unfaithful confidence. Some previous works fine-tuned language models for better ICL performance with enormous datasets and computing costs. In this paper, we propose NoisyICL, simply perturbing the model parameters by random noises to strive for better performance and calibration. Our experiments on two models and 12 downstream datasets show that NoisyICL can help ICL produce more accurate predictions. Our further analysis indicates that NoisyICL enables the model to provide more fair predictions, and also with more faithful confidence. Therefore, we believe that NoisyICL is an effective calibration of ICL. Our experimental code is uploaded to Github.

翻译：上下文学习（ICL）因较高的先验偏差和不可靠的置信度，面临性能欠佳与校准不足的问题。此前一些研究通过微调语言模型来提升ICL性能，但需耗费海量数据集和计算成本。本文提出NoisyICL方法，通过向模型参数添加随机噪声这一简单扰动，以改善性能与校准效果。我们在两种模型和12个下游数据集上的实验表明，NoisyICL能帮助ICL生成更准确的预测。进一步分析显示，NoisyICL可使模型提供更公平的预测，并具有更可靠的置信度。因此，我们认为NoisyICL是一种有效的ICL校准方法。实验代码已上传至Github。