Aspect-based Sentiment Analysis (ABSA) is an important sentiment analysis task, which aims to determine the sentiment polarity towards an aspect in a sentence. Due to the expensive and limited labeled data, data generation (DG) has become the standard for improving the performance of ABSA. However, current DG methods usually have some shortcomings: 1) poor fluency and coherence, 2) lack of diversity of generated data, and 3) reliance on some existing labeled data, hindering its applications in real-world scenarios. With the advancement of large language models (LLMs), LLM-based DG has the potential to solve the above issues. Unfortunately, directly prompting LLMs struggles to generate the desired pseudo-label ABSA data, as LLMs are prone to hallucinations, leading to undesired data generation. To this end, we propose a systematic Iterative Data Generation framework, namely IDG, to boost the performance of ABSA. The core of IDG is to make full use of the powerful abilities (i.e., instruction-following, in-context learning and self-reflection) of LLMs to iteratively generate more fluent and diverse pseudo-label data, starting from an unsupervised sentence corpus. Specifically, IDG designs a novel iterative data generation mechanism and a self-reflection data filtering module to tackle the challenges of unexpected data generation caused by hallucinations. Extensive experiments on four widely-used ABSA benchmarks show that IDG brings consistent and significant performance gains among five baseline ABSA models. More encouragingly, the synthetic data generated by IDG can achieve comparable or even better performance against the manually annotated data.

翻译：基于方面情感分析（ABSA）是一项重要的情感分析任务，其目标在于判定句子中针对特定方面的情感极性。由于标注数据成本高昂且数量有限，数据生成（DG）已成为提升ABSA性能的标准方法。然而，现有DG方法通常存在以下不足：1）生成数据的流畅性与连贯性较差；2）生成数据缺乏多样性；3）依赖部分现有标注数据，限制了其在真实场景中的应用。随着大语言模型（LLMs）的发展，基于LLM的DG方法有望解决上述问题。但直接提示LLMs生成伪标签ABSA数据存在困难，因为LLMs易产生幻觉现象，导致生成不符合预期的数据。为此，我们提出一种系统化的迭代数据生成框架IDG，以提升ABSA性能。IDG的核心在于充分利用LLMs的强大能力（包括指令遵循、上下文学习和自我反思），从无监督的句子语料库出发，迭代生成更流畅、更多样化的伪标签数据。具体而言，IDG设计了新颖的迭代数据生成机制与自我反思数据过滤模块，以应对因模型幻觉导致异常数据生成的挑战。在四个广泛使用的ABSA基准数据集上的大量实验表明，IDG能为五种基线ABSA模型带来持续且显著的性能提升。更令人鼓舞的是，IDG生成的合成数据性能可与人工标注数据相媲美，甚至表现更优。