混合抽样方法提升科学领域信息抽取效果
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains
- 结合代表性采样与检索策略，动态优化上下文示例选择
- 在三个科学数据集上表现优于传统方法，尤其对难识别实体类型提升明显
- 适合需要高效标注的科研场景，尤其在数据稀缺时
大型语言模型具备上下文学习能力，为科学信息抽取提供了新路径，尤其适用于训练数据不足且标注成本高的场景。由于上下文示例的选择直接影响性能，设计高效的样本选取方法至关重要。本文提出STAYKATE，一种静态-动态混合选择方法，融合主动学习中的代表性采样与主流检索式方法的优势。在三个领域特定数据集上的实验表明，STAYKATE显著优于传统监督方法及现有选择策略，尤其在其他方法难以处理的实体类型上表现突出。
原文摘要 · Abstract (English)
Large language models (LLMs) demonstrate the ability to learn in-context, offering a potential solution for scientific information extraction, which often contends with challenges such as insufficient training data and the high cost of annotation processes. Given that the selection of in-context examples can significantly impact performance, it is crucial to design a proper method to sample the efficient ones. In this paper, we propose STAYKATE, a static-dynamic hybrid selection method that combines the principles of representativeness sampling from active learning with the prevalent retrieval-based approach. The results across three domain-specific datasets indicate that STAYKATE outperforms both the traditional supervised methods and existing selection methods. The enhancement in performance is particularly pronounced for entity types that other methods pose challenges.
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