ChatGPT 作为组合泛化的数据增强方法：开放意图检测案例研究
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
AI总结：
本研究针对开放意图检测的组合泛化挑战，提出用ChatGPT生成合成数据做数据增强的方法，经多基准验证性能优于现有技术，证明大语言模型在自然语言理解数据增强中的潜力。
摘要：
开放意图检测是自然语言理解的核心方向之一，旨在识别用户生成文本中此前未见过的意图类别。尽管该领域已取得一定进展，但在处理语言成分的新组合方面仍存在挑战，而这一能力对组合泛化至关重要。本文开展了一项案例研究，探索将 ChatGPT 用作数据增强技术，以提升开放意图检测任务中的组合泛化能力。我们首先讨论了现有基准在评估该问题上的局限性，指出有必要构建专门用于解决开放意图检测中组合泛化问题的数据集。通过将 ChatGPT 生成的合成数据融入训练流程，我们证明该方法能有效提升模型性能。在多个基准上的严格评估显示，我们的方法优于现有技术，显著增强了开放意图检测能力。本研究结果凸显了 ChatGPT 这类大语言模型在自然语言理解任务中用于数据增强的潜力。
英文摘要：
Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text. Despite the progress made in this field, challenges persist in handling new combinations of language components, which is essential for compositional generalization. In this paper, we present a case study exploring the use of ChatGPT as a data augmentation technique to enhance compositional generalization in open intent detection tasks. We begin by discussing the limitations of existing benchmarks in evaluating this problem, highlighting the need for constructing datasets for addressing compositional generalization in open intent detection tasks. By incorporating synthetic data generated by ChatGPT into the training process, we demonstrate that our approach can effectively improve model performance. Rigorous evaluation of multiple benchmarks reveals that our method outperforms existing techniques and significantly enhances open intent detection capabilities. Our findings underscore the potential of large language models like ChatGPT for data augmentation in natural language understanding tasks.
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。
