arXiv 2409.096152024-09-15cs.CLcs.AI

通过基于推理依据的协同少样本提示增强文本标注

Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting

Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；College of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）

Jianfei Wu, Xubin Wang, Weijia Jia

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AI总结：

针对传统文本标注成本高、易有偏差的问题，提出基于推理依据的协同少样本提示技术，经多数据集多模型验证该方法优于传统少样本等基线，为高效文本标注提供框架参考。

摘要：

传统数据标注流程通常人力成本高、耗时长且易受人为偏差影响，这让复杂度日益提升的数据集管理变得困难。本研究探索了大语言模型（LLMs）作为自动化数据标注工具提升标注任务效率与一致性的潜力。我们采用基于推理依据的协同少样本提示技术，旨在提升LLMs在文本标注任务中的表现。我们在四个基准数据集上对6个LLM开展了严格评估，对比了7种不同方法。结果表明，协同方法的表现始终优于传统少样本技术及其他基线方法，在复杂标注任务中优势尤为明显。本研究为利用协同学习方法应对高难度文本标注任务提供了宝贵见解与稳健框架。

英文摘要：

The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of six LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks.

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