Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples. However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. Then we empirically show that prompts with higher bias always lead to unsatisfactory predictive quality. Based on this observation, we propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning. We perform comprehensive experiments with state-of-the-art mainstream models such as GPT-3 on various downstream tasks. Our results indicate that our method can enhance the model's in-context learning performance in an effective and interpretable manner.

翻译：大型语言模型已展现出令人惊讶的上下文学习能力，即通过基于少量输入输出示例构建的提示条件，可直接应用于解决众多下游任务。然而，先前研究表明，训练示例、示例顺序和提示格式的差异可能导致上下文学习出现高度不稳定性。因此，构建合适的提示对于提升上下文学习性能至关重要。本文从预测偏差的视角重新审视该问题：首先引入一种度量标准，以评估固定提示相对于标签或给定属性的预测偏差，随后通过实验证明高偏差提示始终会导致较差的预测质量。基于这一发现，我们提出一种基于贪心搜索的新型搜索策略，用于识别近最优提示以改进上下文学习性能。我们采用GPT-3等最先进的主流模型在多种下游任务上开展全面实验，结果表明，该方法能够以有效且可解释的方式增强模型的上下文学习性能。