The vast availability of free data has been critical to the success of large language models (LLMs). With the widespread use of LLMs, more and more concerns have been raised about the unauthorized use of publicly available data. To protect data from unauthorized use for training models, researchers have proposed adding imperceptible perturbations into image data so that models would be misled by the generated shortcut features and cannot mine information from these images. However, due to the inherent discrete property and semantic complexity of texts, directly applying these methods to text will cause semantic changes, resulting in meaningless shortcut features being constructed. To tackle this problem, in this article, we design a novel Unlearnable text examples generation algorithm via syntax-oriented shortcut (UTE-SS) by incorporating the syntactic structure of texts. Specifically, we propose a syntax template generator (STG) to generate the optimal perturbing syntax for a given category, which will realize imperceptible perturbations. Then, a perturbing text generator (PTG) is designed to perturb the in-class texts with the selected syntax template to stably deviate from the original texts. Along this line, models will be misled to learn the shortcut between the syntax template and the category, so as to keep text examples unlearnable. Extensive experiments over eight advanced Transformer-based pretrained language models (PLMs) on four different natural language processing (NLP) tasks demonstrate the effectiveness and flexibility of our proposed algorithm. Our method is easy to implement, and the code is publicly available at https://github.com/libolb/UTE-SS.

Conditional Independence (CI) tests play an essential role in causal discovery from observational data, enabling the measurement of independence between two nodes. However, traditional CI tests ignore the imbalanced occurrence probabilities of node values, which may affect the accuracy of determining independence between nodes. To address this problem, we first introduce a new concept of the Node-imbalance phenomenon to describe the imbalance of node values in the Bayesian network data and analyze the influence of the Node-imbalance phenomenon on the traditional CI tests, then we propose a Weight-Based Conditional Independence (WCI) test to improve the accuracy of CI tests in the presence of Node-imbalance. In the experiments, we verify that WCI effectively measures the dependency between nodes in the Node-imbalance phenomenon compared with the traditional independence tests, and the state-of-the-art causal discovery algorithms reduce the number of false causal orientations through WCI.

Causal feature selection has recently attracted much more attention because it can improve the interpretability of predictive models. However, the existing causal feature selection framework needs to discover the PC (i.e., parents and children) of each variable in the PC of a target variable for spouses discovery, which is time-consuming on high-dimensional data. To tackle this issue, we propose a novelCausalFeatureSelection framework with efficient spouses discovery, called CFS. Specifically, by exploiting the dependency change property between a variable and its non-PC, the proposed framework only discovers the PC of the variables in some children of the target variable for spouses discovery. Furthermore, based on the proposed CFS framework and existing PC discovery algorithms, we propose four new causal feature selection algorithms. Using benchmark Bayesian networks and real-world datasets, we experimentally validated the efficiency and accuracy of the proposed algorithms compared with seven state-of-the-art causal feature selection algorithms.

Causal feature selection has received increasing attention in recent years. However, the state-of-the-art causal feature selection algorithms use the conditional independence tests, which require enumerating conditioning sets, leading to an exponential increase in computational complexity along with an increase in feature space. To address this problem, in this paper, we theoretically analyze the unique performance of causal features in mutual information, and propose a novelCausalFeatureSelection algorithm usingMutualInformation, called CFS-MI. Specifically, CFS-MI separately instantiates the pairwise comparison of mutual information in two stages to reduce computational complexity, and thus improves the efficiency on high-dimensional data. Extensive experiments on 5 benchmark Bayesian networks and 16 real-world datasets validate that CFS-MI has comparable accuracy compared to 7 state-of-the-art causal feature selection algorithms, while presenting more superior computational efficiency.