Tao Xiong

AILAW Journal 2026 Journal Article

The trade-off between robustness and reliability in chinese legal large language models: an empirical study

• Yang Liu
• Xukai Liu
• Haozhen Huang
• Fanfei Yu
• Tao Xiong
• Xiaoyiqi Xia
• Bohao You
• Jinqi Wu

Abstract Legal large language models (LLMs) deployed in high-stakes judicial settings must exhibit robustness against non-substantive linguistic variations while preserving acute sensitivity to legally determinative facts and norms. This study investigates this robustness–reliability trade-off within the context of Chinese legal tasks. We curate a dataset of 5, 000 Chinese judicial question–answer pairs and generate semantic-preserving adversarial rewrites, retaining only those validated by an embedding-based semantic consistency filter. Holding the total training budget and fine-tuning protocol constant, we fine-tune model variants that differ exclusively in their injection ratio of these verified rewrites, establishing seven distinct injection groups (G0–G6). We evaluate model reliability utilizing a composite protocol that incorporates objective accuracy on exam-style questions, expert evaluations of open-ended responses, and embedding-based semantic similarity. For trademark infringement reasoning tasks, we additionally assess verdict accuracy and rationale quality. Across varying model capacities (4B, 20B, and 32B backbones, including Qwen3-4B, GPT-OSS-20B, and Qwen3-VL-32B-Instruct) and both evaluated tasks, our findings reveal an inverted‑U relationship: moderate robustness data injection enhances reliability, whereas excessive injection degrades overall performance and induces characteristic failure modes, such as the attenuation of legally salient distinctions, the generation of boilerplate rationales, and overly cautious abstention. These findings substantiate “moderate robustness injection” as a practical heuristic and underscore a broader principle of differential sensitivity—achieving insensitivity to superficial variations without blunting the model’s sensitivity to legally decisive elements.

EAAI Journal 2015 Journal Article

Hybrid filter–wrapper feature selection for short-term load forecasting

• Zhongyi Hu
• Yukun Bao
• Tao Xiong
• Raymond Chiong

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter–wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts.

AAAI Conference 2014 Conference Paper

A Joint Optimization Model for Image Summarization Based on Image Content and Tags

• Hongliang Yu
• Zhi-Hong Deng
• Yunlun Yang
• Tao Xiong

As an effective technology for navigating a large number of images, image summarization is becoming a promising task with the rapid development of image sharing sites and social networks. Most existing summarization approaches use the visual-based features for image representation without considering tag information. In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images. Our model generates the summary images which can best reconstruct the original collection. Based on the assumption that an image with representative content should also have typical tags, we introduce a similarity-inducing regularizer to our model. Furthermore, we impose the lasso penalty on the objective function to yield a concise summary set. Extensive experiments demonstrate our model outperforms the state-of-the-art approaches.

JMLR Journal 2006 Journal Article

Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis

• Jieping Ye
• Tao Xiong

Dimensionality reduction is an important pre-processing step in many applications. Linear discriminant analysis (LDA) is a classical statistical approach for supervised dimensionality reduction. It aims to maximize the ratio of the between-class distance to the within-class distance, thus maximizing the class discrimination. It has been used widely in many applications. However, the classical LDA formulation requires the nonsingularity of the scatter matrices involved. For undersampled problems, where the data dimensionality is much larger than the sample size, all scatter matrices are singular and classical LDA fails. Many extensions, including null space LDA (NLDA) and orthogonal LDA (OLDA), have been proposed in the past to overcome this problem. NLDA aims to maximize the between-class distance in the null space of the within-class scatter matrix, while OLDA computes a set of orthogonal discriminant vectors via the simultaneous diagonalization of the scatter matrices. They have been applied successfully in various applications. In this paper, we present a computational and theoretical analysis of NLDA and OLDA. Our main result shows that under a mild condition which holds in many applications involving high-dimensional data, NLDA is equivalent to OLDA. We have performed extensive experiments on various types of data and results are consistent with our theoretical analysis. We further apply the regularization to OLDA. The algorithm is called regularized OLDA (or ROLDA for short). An efficient algorithm is presented to estimate the regularization value in ROLDA. A comparative study on classification shows that ROLDA is very competitive with OLDA. This confirms the effectiveness of the regularization in ROLDA. [abs] [ pdf ][ bib ] &copy JMLR 2006. ( edit, beta )

ICML Conference 2006 Conference Paper

Null space versus orthogonal linear discriminant analysis

• Jieping Ye
• Tao Xiong

NeurIPS Conference 2004 Conference Paper

Efficient Kernel Discriminant Analysis via QR Decomposition

• Tao Xiong
• Jieping Ye
• Qi Li
• Ravi Janardan
• Vladimir Cherkassky

Linear Discriminant Analysis (LDA) is a well-known method for fea- ture extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algo- rithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and space. However, LDA/QR is based on linear projection, which may not be suitable for data with nonlinear structure. This paper first proposes an algorithm called KDA/QR, which extends the LDA/QR algorithm to deal with nonlin- ear data by using the kernel operator. Then an efficient approximation of KDA/QR called AKDA/QR is proposed. Experiments on face image data show that the classification accuracy of both KDA/QR and AKDA/QR are competitive with Generalized Discriminant Analysis (GDA), a gen- eral kernel discriminant analysis algorithm, while AKDA/QR has much lower time and space costs. 1 Introduction Linear Discriminant Analysis [3] is a wellknown method for dimension reduction. It has been used widely in many applications such as face recognition [2]. Classical LDA aims to find optimal transformation by minimizing the within-class distance and maximizing the between-class distance simultaneously, thus achieving maximum discrimination. The optimal transformation can be readily computed by computing the eigen-decomposition on the scatter matrices. Although LDA works well for linear problems, it may be less effective when severe non- linearity is involved. To deal with such a limitation, nonlinear extensions through kernel functions have been proposed. The main idea of kernel-based methods is to map the input data to a feature space through a nonlinear mapping, where the inner products in the feature space can be computed by a kernel function without knowing the nonlinear mapping explic- itly [9]. Kernel Principal Component Analysis (KPCA) [10], Kernel Fisher Discriminant Analysis (KFDA) [7] and Generalized Discriminant Analysis (GDA) [1] are, respectively, kernel-based nonlinear extensions of the well known PCA, FDA and LDA methods. To our knowledge, there are few efficient algorithms for general kernel based discriminant algorithms -- most known algorithms effectively scale as O(n3) where n is the sample size. In [6, 8], S. Mika et al. made a first attempt to speed up KFDA through a greedy approximation technique. However the algorithm was developed to handle the binary clas- sification problem. For multi-class problem, the authors suggested the one against the rest scheme by considering all two-class problems. Recently, an efficient variant of LDA, namely LDA/QR, was proposed in [11, 12]. The essence of LDA/QR is the utilization of QR-decomposition on a small size matrix. The time complexity of LDA/QR is linear in the size of the training data, as well as the number of dimensions of the data. Moreover, experiments in [11, 12] show that the classification accuracy of LDA/QR is competitive with other LDA algorithms. In this paper, we first propose an algorithm, namely KDA/QR1, which is a nonlinear exten- sion of LDA/QR. Since KDA/QR involves the whole kernel matrix, which is not scalable for large datasets, we also propose an approximation of KDA/QR, namely AKDA/QR. A distinct property of AKDA/QR is that it scales as O(ndc), where n is the size of the data, d is the dimension of the data, and c is the number of classes. We apply the proposed algorithms on face image datasets and compare them with LDA/QR, and Generalized Discriminant Analysis (GDA) [1], a general method for kernel discrim- inant analysis. Experiments show that: (1) AKDA/QR is competitive with KDA/QR and GDA in classification; (2) both KDA/QR and AKDA/QR outperform LDA/QR in classifi- cation; and (3) AKDA/QR has much lower costs in time and space than GDA.