Large language models (LLMs) are increasingly being used for generating text
in a variety of use cases, including journalistic news articles. Given the
potential malicious nature in which these LLMs can be used to generate
disinformation at scale, it is important to build effective detectors for such
AI-generated text. Given the surge in development of new LLMs, acquiring
labeled training data for supervised detectors is a bottleneck. However, there
might be plenty of unlabeled text data available, without information on which
generator it came from. In this work we tackle this data problem, in detecting
AI-generated news text, and frame the problem as an unsupervised domain
adaptation task. Here the domains are the different text generators, i.e. LLMs,
and we assume we have access to only the labeled source data and unlabeled
target data. We develop a Contrastive Domain Adaptation framework, called
ConDA, that blends standard domain adaptation techniques with the
representation power of contrastive learning to learn domain invariant
representations that are effective for the final unsupervised detection task.
Our experiments demonstrate the effectiveness of our framework, resulting in
average performance gains of 31.7% from the best performing baselines, and
within 0.8% margin of a fully supervised detector. All our code and data is
available at this https URL

利用对比性领域自适应框架 ConDA，本研究解决了在检测人工智能生成的新闻文本中未标注文本数据的问题，并取得了平均性能提升 31.7％，与完全监督的检测器相差不到 0.8％的结果。

ConDA: 基於對比學習的 AI 生成文本檢測的域適應

ConDA: Contrastive Domain Adaptation for AI-generated Text Detection

The limited scale of annotated data constraints existing context-dependent
text-to-SQL models because of the complexity of labeling. The data augmentation
method is a commonly used method to solve this problem. However, the data
generated by current augmentation methods often lack diversity. In this paper,
we introduce ConDA, which generates interactive questions and corresponding SQL
results. We designed the SQL dialogue state to enhance the data diversity
through the state transition. Meanwhile, we also present a filter method to
ensure the data quality by a grounding model. Additionally, we utilize a
grounding model to identify and filter low-quality questions that mismatch the
state information. Experimental results on the SParC and CoSQL datasets show
that ConDA boosts the baseline model to achieve an average improvement of
$3.3\%$ on complex questions. Moreover, we analyze the augmented data, which
reveals that the data generated by ConDA are of high quality in both SQL
template hardness and types, turns, and question consistency.

本文介绍了一种名为 ConDA 的数据扩充方法，通过交互式问题和相应的 SQL 结果生成数据，利用 SQL 对话状态增强数据多样性，同时利用基础模型确保数据质量。实验结果表明，ConDA 使基础模型的性能平均提升了 3.3％，且生成的数据质量较高。

基于上下文的可控数据增强技术在文本转 SQL 中的应用

Controllable Data Augmentation for Context-Dependent Text-to-SQL

Transferring knowledge learned from the labeled source domain to the raw
target domain for unsupervised domain adaptation (UDA) is essential to the
scalable deployment of autonomous driving systems. State-of-the-art methods in
UDA often employ a key idea: utilizing joint supervision signals from both
source and target domains for self-training. In this work, we improve and
extend this aspect. We present ConDA, a concatenation-based domain adaptation
framework for LiDAR segmentation that: 1) constructs an intermediate domain
consisting of fine-grained interchange signals from both source and target
domains without destabilizing the semantic coherency of objects and background
around the ego-vehicle; and 2) utilizes the intermediate domain for
self-training. To improve the network training on the source domain and
self-training on the intermediate domain, we propose an anti-aliasing
regularizer and an entropy aggregator to reduce the negative effect caused by
the aliasing artifacts and noisy pseudo labels. Through extensive studies, we
demonstrate that ConDA significantly outperforms prior arts in mitigating
domain gaps.

本文提出了一种基于联合监督信号自训练的 LiDAR Segmentation 领域自适应方法 ConDA，通过构建源域和目标域的中间领域并采用熵聚合器和抗锯齿正则化方法，实现了有效减少噪音伪标签对模型训练的负面影响，并在减轻域间差异方面取得了非常显著的提升。