Despite the prevalence of tabular datasets, few-shot learning remains
under-explored within this domain. Existing few-shot methods are not directly
applicable to tabular datasets due to varying column relationships, meanings,
and permutational invariance. To address these challenges, we propose FLAT-a
novel approach to tabular few-shot learning, encompassing knowledge sharing
between datasets with heterogeneous feature spaces. Utilizing an encoder
inspired by Dataset2Vec, FLAT learns low-dimensional embeddings of datasets and
their individual columns, which facilitate knowledge transfer and
generalization to previously unseen datasets. A decoder network parametrizes
the predictive target network, implemented as a Graph Attention Network, to
accommodate the heterogeneous nature of tabular datasets. Experiments on a
diverse collection of 118 UCI datasets demonstrate FLAT's successful
generalization to new tabular datasets and a considerable improvement over the
baselines.

对于标签数据集，目前还未深入探索少样本学习。现有的少样本学习方法不适用于标签数据集，因为其存在不同的列关系、意义和排列不变性。为了解决这些挑战，我们提出了一种名为 FLAT 的新型标签少样本学习方法，涵盖了异构特征空间下数据集之间的知识共享。利用 Dataset2Vec 启发的编码器，FLAT 学习了数据集和其各个列的低维嵌入，从而促进了对以前未见过的数据集的知识传递和泛化。一个解码器网络对预测目标网络进行参数化，作为图注意网络的实现方式，以适应标签数据集的异构性质。对 118 个 UCI 数据集进行的实验证明了 FLAT 在新的标签数据集上的成功泛化，并且相对于基准模型有了显著的改进。

跨异构特征空间的表格式少样本泛化

Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces

Neural language models show vulnerability to adversarial examples which are
semantically similar to their original counterparts with a few words replaced
by their synonyms. A common way to improve model robustness is adversarial
training which follows two steps-collecting adversarial examples by attacking a
target model, and fine-tuning the model on the augmented dataset with these
adversarial examples. The objective of traditional adversarial training is to
make a model produce the same correct predictions on an original/adversarial
example pair. However, the consistency between model decision-makings on two
similar texts is ignored. We argue that a robust model should behave
consistently on original/adversarial example pairs, that is making the same
predictions (what) based on the same reasons (how) which can be reflected by
consistent interpretations. In this work, we propose a novel feature-level
adversarial training method named FLAT. FLAT aims at improving model robustness
in terms of both predictions and interpretations. FLAT incorporates variational
word masks in neural networks to learn global word importance and play as a
bottleneck teaching the model to make predictions based on important words.
FLAT explicitly shoots at the vulnerability problem caused by the mismatch
between model understandings on the replaced words and their synonyms in
original/adversarial example pairs by regularizing the corresponding global
word importance scores. Experiments show the effectiveness of FLAT in improving
the robustness with respect to both predictions and interpretations of four
neural network models (LSTM, CNN, BERT, and DeBERTa) to two adversarial attacks
on four text classification tasks. The models trained via FLAT also show better
robustness than baseline models on unforeseen adversarial examples across
different attacks.

本文提出了一种名为 FLAT 的特征级对抗性训练方法，该方法旨在通过正则化全局词重要性分数来调节模型在替换词及其同义词的原始 / 对抗性示例对中的理解保持一致，从而提高模型对预测和解释方面的鲁棒性。

通过预测和解释来提高模型的稳健性的对抗训练

Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation

Recently, the character-word lattice structure has been proved to be
effective for Chinese named entity recognition (NER) by incorporating the word
information. However, since the lattice structure is complex and dynamic, most
existing lattice-based models are hard to fully utilize the parallel
computation of GPUs and usually have a low inference-speed. In this paper, we
propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the
lattice structure into a flat structure consisting of spans. Each span
corresponds to a character or latent word and its position in the original
lattice. With the power of Transformer and well-designed position encoding,
FLAT can fully leverage the lattice information and has an excellent
parallelization ability. Experiments on four datasets show FLAT outperforms
other lexicon-based models in performance and efficiency.

本研究提出 FLAT：Flat-LAttice Transformer，将复杂动态的中文命名实体识别字符 - 词格结构转换成跨度组成的平面结构，借助 Transformer 和精心设计的位置编码完全利用格子信息，具有出色的并行化能力和性能效率。在四个数据集上的实验证明，FLAT 在性能和效率方面均优于其他基于词典的模型。