从随机到知情的数据选择：一种基于多样性的方法以优化人工标注与少样本学习
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning
AI总结：
针对随机标注效率低且在不平衡数据中偏向多数类的问题，本文提出一种自动、知情且基于多样性的数据选择架构，以更少人工标注构建少样本数据集并提升模型性能。
摘要：
自然语言处理中的一个主要挑战是获取用于监督学习的标注数据。一种选择是使用众包平台进行数据标注。然而，众包会带来与标注者经验、一致性和偏见相关的问题。另一种选择是使用零样本方法，但与少样本或完全监督方法相比，这类方法又存在局限。由大型语言模型推动的最新进展展现出潜力，但在数据极其有限的专业领域中难以适应。因此，最常见的方法是由人类自身随机标注一组数据点，以构建初始数据集。但是，随机抽样待标注数据往往效率低下，因为它忽视了数据特征和模型的特定需求。在处理不平衡数据集时，情况会进一步恶化，因为随机抽样往往严重偏向多数类，从而产生过多的标注数据。为解决这些问题，本文提出一种自动且知情的数据选择架构，用于构建面向少样本学习的小型数据集。我们的方案在减少供人工标注的数据数量的同时最大化所选数据的多样性，并提升模型性能。
英文摘要：
A major challenge in Natural Language Processing is obtaining annotated data for supervised learning. An option is the use of crowdsourcing platforms for data annotation. However, crowdsourcing introduces issues related to the annotator's experience, consistency, and biases. An alternative is to use zero-shot methods, which in turn have limitations compared to their few-shot or fully supervised counterparts. Recent advancements driven by large language models show potential, but struggle to adapt to specialized domains with severely limited data. The most common approaches therefore involve the human itself randomly annotating a set of datapoints to build initial datasets. But randomly sampling data to be annotated is often inefficient as it ignores the characteristics of the data and the specific needs of the model. The situation worsens when working with imbalanced datasets, as random sampling tends to heavily bias towards the majority classes, leading to excessive annotated data. To address these issues, this paper contributes an automatic and informed data selection architecture to build a small dataset for few-shot learning. Our proposal minimizes the quantity and maximizes diversity of data selected for human annotation, while improving model performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
