Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is a key open challenge in creating reliable NLG systems. In this work, we introduce a framework to identify and remove low-quality training instances that lead to undesirable outputs, such as faithfulness errors in text summarization. We show that existing approaches for error tracing, such as gradient-based influence measures, do not perform reliably for detecting faithfulness errors in NLG datasets. We overcome the drawbacks of existing error tracing methods through a new, contrast-based estimate that compares undesired generations to human-corrected outputs. Our proposed method can achieve a mean average precision of 0.93 at detecting known data errors across synthetic tasks with known ground truth, substantially outperforming existing approaches. Using this approach and re-training models on cleaned data leads to a 70% reduction in entity hallucinations on the NYT dataset and a 55% reduction in semantic errors on the E2E dataset.

翻译：近期研究表明，噪声数据和错误标注是导致自然语言生成任务中出现幻觉及不忠实输出的核心原因。因此，识别并移除这些训练样本成为构建可靠NLG系统的关键开放性挑战。本文提出一个框架，用于识别并移除导致不良输出的低质量训练实例（例如文本摘要中的忠实性错误）。我们发现，现有误差追踪方法（如基于梯度的影响力测量）在检测NLG数据集中的忠实性错误时可靠性不足。我们通过一种新的基于对比的估计方法克服了现有误差追踪技术的缺陷——该方法将不良生成结果与人工修正输出进行对比。在已知真实标注的合成任务中，我们的方法检测已知数据错误的平均精度均值可达0.93，显著优于现有方法。采用该策略并在清洗后的数据上重新训练模型，可使NYT数据集中的实体幻觉减少70%，E2E数据集中的语义错误减少55%。