Attributing answer text to its source document for information-seeking
questions is crucial for building trustworthy, reliable, and accountable
systems. We formulate a new task of post-hoc answer attribution for long
document comprehension (LDC). Owing to the lack of long-form abstractive and
information-seeking LDC datasets, we refactor existing datasets to assess the
strengths and weaknesses of existing retrieval-based and proposed answer
decomposition and textual entailment-based optimal selection attribution
systems for this task. We throw light on the limitations of existing datasets
and the need for datasets to assess the actual performance of systems on this
task.

长文理解中对答案文本进行归因对于建立可信赖、可靠和负责任的系统至关重要，而缺乏长文崭新性和信息查找性的归因数据集使得我们需要重构现有数据集以评估现有基于检索和提出的答案分解和文本蕴含为基础的最佳选择归因系统在这一任务上的优势和不足，同时也突显了现有数据集的限制和对于评估系统在该任务上实际性能的需求。

面向可信、可靠的长文档理解的事后答案归因：任务、洞见和挑战

Post-Hoc Answer Attribution for Grounded and Trustworthy Long Document  Comprehension: Task, Insights, and Challenges

Industrial anomaly detection (IAD) has garnered significant attention and
experienced rapid development. However, the recent development of IAD approach
has encountered certain difficulties due to dataset limitations. On the one
hand, most of the state-of-the-art methods have achieved saturation (over 99%
in AUROC) on mainstream datasets such as MVTec, and the differences of methods
cannot be well distinguished, leading to a significant gap between public
datasets and actual application scenarios. On the other hand, the research on
various new practical anomaly detection settings is limited by the scale of the
dataset, posing a risk of overfitting in evaluation results. Therefore, we
propose a large-scale, Real-world, and multi-view Industrial Anomaly Detection
dataset, named Real-IAD, which contains 150K high-resolution images of 30
different objects, an order of magnitude larger than existing datasets. It has
a larger range of defect area and ratio proportions, making it more challenging
than previous datasets. To make the dataset closer to real application
scenarios, we adopted a multi-view shooting method and proposed sample-level
evaluation metrics. In addition, beyond the general unsupervised anomaly
detection setting, we propose a new setting for Fully Unsupervised Industrial
Anomaly Detection (FUIAD) based on the observation that the yield rate in
industrial production is usually greater than 60%, which has more practical
application value. Finally, we report the results of popular IAD methods on the
Real-IAD dataset, providing a highly challenging benchmark to promote the
development of the IAD field.

提出了一个大规模、真实世界的多视角工业异常检测数据集（Real-IAD），包含 30 种对象的 15 万张高清图片，相比现有数据集大一个数量级。该数据集具有更大范围的缺陷区域和比例，具有更高的挑战性。在提供了常用 IAD 方法在该数据集上的结果后，为推动 IAD 领域的发展提供了一个极具挑战性的基准。