Data augmentations are useful in closing the sim-to-real domain gap when
training on synthetic data. This is because they widen the training data
distribution, thus encouraging the model to generalize better to other domains.
Many image augmentation techniques exist, parametrized by different settings,
such as strength and probability. This leads to a large space of different
possible augmentation policies. Some policies work better than others for
overcoming the sim-to-real gap for specific datasets, and it is unclear why.
This paper presents two different interpretable metrics that can be combined to
predict how well a certain augmentation policy will work for a specific
sim-to-real setting, focusing on object detection. We validate our metrics by
training many models with different augmentation policies and showing a strong
correlation with performance on real data. Additionally, we introduce
GeneticAugment, a genetic programming method that can leverage these metrics to
automatically design an augmentation policy for a specific dataset without
needing to train a model.

使用可解释的度量方法，结合遗传编程技术，预测适用于特定 sim-to-real 场景的数据增强策略，并在无需训练模型的情况下为特定数据集设计数据增强策略。

基因学习用于设计模拟到真实数据增强

Genetic Learning for Designing Sim-to-Real Data Augmentations

Faithfully correcting factual errors is critical for maintaining the
integrity of textual knowledge bases and preventing hallucinations in
sequence-to-sequence models. Drawing on humans' ability to identify and correct
factual errors, we present a zero-shot framework that formulates questions
about input claims, looks for correct answers in the given evidence, and
assesses the faithfulness of each correction based on its consistency with the
evidence. Our zero-shot framework outperforms fully-supervised approaches, as
demonstrated by experiments on the FEVER and SciFact datasets, where our
outputs are shown to be more faithful. More importantly, the decomposability
nature of our framework inherently provides interpretability. Additionally, to
reveal the most suitable metrics for evaluating factual error corrections, we
analyze the correlation between commonly used metrics with human judgments in
terms of three different dimensions regarding intelligibility and faithfulness.

该论文提出了一种零 - shot 框架，用于纠正文本知识库中的事实错误，具有可解释性，并使用不同的方法进行度量，改善了已有的模型。

零样式忠实事实误差校正

Zero-shot Faithful Factual Error Correction

Evaluating open-domain dialogue systems is difficult due to the diversity of
possible correct answers. Automatic metrics such as BLEU correlate weakly with
human annotations, resulting in a significant bias across different models and
datasets. Some researchers resort to human judgment experimentation for
assessing response quality, which is expensive, time consuming, and not
scalable. Moreover, judges tend to evaluate a small number of dialogues,
meaning that minor differences in evaluation configuration may lead to
dissimilar results. In this paper, we present interpretable metrics for
evaluating topic coherence by making use of distributed sentence
representations. Furthermore, we introduce calculable approximations of human
judgment based on conversational coherence by adopting state-of-the-art
entailment techniques. Results show that our metrics can be used as a surrogate
for human judgment, making it easy to evaluate dialogue systems on large-scale
datasets and allowing an unbiased estimate for the quality of the responses.

本文提出了基于分布式句子表示的可解释计算指标，用于评估开放领域对话系统的主题、话题的连贯性，减少了人工判断实验的时间成本和不可伸缩性，并结合先进的蕴含技术，介绍了可计算的人工判断形式，实现了对大规模数据集上对话质量的无偏估计。