Arrow Research search

Author name cluster

Weidong Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

12 papers
1 author row

Possible papers

12

TIST Journal 2025 Journal Article

The Evaluation Framework and Benchmark for Large Language Models in the Government Affairs Domain

  • Shuo Liu
  • Lin Zhang
  • Weidong Liu
  • Jianfeng Zhang
  • Donghui Gao
  • Xiaofeng Jia

The rapid evolution of AI has driven advancements across numerous sectors. In the domain of government affairs, large language models (LLMs) hold significant potential for applications such as policy analysis, data processing, and decision support. However, their adoption in government settings faces considerable challenges, including data accessibility issues, the absence of standardized evaluation criteria, and concerns regarding model accuracy, reliability, and security. To address these challenges, we propose a comprehensive evaluation framework specifically designed for LLMs in government affairs. Built on modular principles, this framework ensures adaptability across various industries. Additionally, we introduce the Multi-Scenario Government Affairs Benchmark (MSGABench 1 ) dataset, a Chinese-language dataset specifically crafted to meet the practical needs of government professionals. Employing the proposed framework and the MSGA dataset, we conducted an empirical evaluation of 15 prominent LLMs, revealing critical insights: (1) Performance: Many models demonstrated low accuracy and reliability, particularly under minor input variations, with some dropping below 35% accuracy, whereas GPT-4 achieved above 95% reliability; (2) Security and Compliance: Significant concerns were identified, including privacy vulnerabilities, legal compliance risks, and persistent biases, which may hinder secure deployments in government contexts; (3) Task Avoidance: Certain models exhibited excessive caution, often avoiding responses to basic tasks like document classification and government-related inquiries, which restricts their usability. These findings highlight essential limitations and opportunities for improvement, contributing to the safe and effective application of LLMs in the government sector.

Details DOI

JMLR Journal 2024 Journal Article

Distributed Estimation on Semi-Supervised Generalized Linear Model

  • Jiyuan Tu
  • Weidong Liu
  • Xiaojun Mao

Semi-supervised learning is devoted to using unlabeled data to improve the performance of machine learning algorithms. In this paper, we study the semi-supervised generalized linear model (GLM) in the distributed setup. In the cases of single or multiple machines containing unlabeled data, we propose two distributed semi-supervised algorithms based on the distributed approximate Newton method. When the labeled local sample size is small, our algorithms still give a consistent estimation, while fully supervised methods fail to converge. Moreover, we theoretically prove that the convergence rate is greatly improved when sufficient unlabeled data exists. Therefore, the proposed method requires much fewer rounds of communications to achieve the optimal rate than its fully-supervised counterpart. In the case of the linear model, we prove the rate lower bound after one round of communication, which shows that rate improvement is essential. Finally, several simulation analyses and real data studies are provided to demonstrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

PDF Details

NeurIPS Conference 2024 Conference Paper

OneBit: Towards Extremely Low-bit Large Language Models

  • Yuzhuang Xu
  • Xu Han
  • Zonghan Yang
  • Shuo Wang
  • Qingfu Zhu
  • Zhiyuan Liu
  • Weidong Liu
  • Wanxiang Che

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit model compressing framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the quantization framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 81% of the non-quantized performance on LLaMA models) with robust training processes when only using 1-bit weight matrices.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

ST$_k$: A Scalable Module for Solving Top-k Problems

  • Hanchen Xia
  • Weidong Liu
  • Xiaojun Mao

The cost of ranking becomes significant in the new stage of deep learning. We propose ST$_k$, a fully differentiable module with a single trainable parameter, designed to solve the Top-k problem without requiring additional time or GPU memory. Due to its fully differentiable nature, ST$_k$ can be embedded end-to-end into neural networks and optimize the Top-k problems within a unified computational graph. We apply ST$_k$ to the Average Top-k Loss (AT$_k$), which inherently faces a Top-k problem. The proposed ST$_k$ Loss outperforms AT$_k$ Loss and achieves the best average performance on multiple benchmarks, with the lowest standard deviation. With the assistance of ST$_k$ Loss, we surpass the state-of-the-art (SOTA) on both CIFAR-100-LT and Places-LT leaderboards.

PDF Details DOI

JMLR Journal 2021 Journal Article

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

  • Jiyuan Tu
  • Weidong Liu
  • Xiaojun Mao
  • Xi Chen

This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines in a distributed learning system. In robust statistics, the median-of-means (MOM) has been a popular approach to hedge against Byzantine failures due to its ease of implementation and computational efficiency. However, the MOM estimator has the shortcoming in terms of statistical efficiency. The first main contribution of the paper is to propose a variance reduced median-of-means (VRMOM) estimator, which improves the statistical efficiency over the vanilla MOM estimator and is computationally as efficient as the MOM. Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures. Theoretically, our distributed algorithm achieves a fast convergence rate with only a constant number of rounds of communications. We also provide the asymptotic normality result for the purpose of statistical inference. To the best of our knowledge, this is the first normality result in the setting of Byzantine-robust distributed learning. The simulation results are also presented to illustrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

PDF Details

IJCAI Conference 2020 Conference Paper

Bayesian Decision Process for Budget-efficient Crowdsourced Clustering

  • Xiaozhou Wang
  • Xi Chen
  • Qihang Lin
  • Weidong Liu

The performance of clustering depends on an appropriately defined similarity between two items. When the similarity is measured based on human perception, human workers are often employed to estimate a similarity score between items in order to support clustering, leading to a procedure called crowdsourced clustering. Assuming a monetary reward is paid to a worker for each similarity score and assuming the similarities between pairs and workers' reliability have a large diversity, when the budget is limited, it is critical to wisely assign pairs of items to different workers to optimize the clustering result. We model this budget allocation problem as a Markov decision process where item pairs are dynamically assigned to workers based on the historical similarity scores they provided. We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph. We provide simulation studies and real data analysis to demonstrate the performance of the proposed method.

PDF Details DOI

JMLR Journal 2020 Journal Article

Distributed High-dimensional Regression Under a Quantile Loss Function

  • Xi Chen
  • Weidong Liu
  • Xiaojun Mao
  • Zhuoyi Yang

This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance can be infinite, we adopt the quantile regression loss function instead of the commonly used squared loss. However, the non-smooth quantile loss poses new challenges to high-dimensional distributed estimation in both computation and theoretical development. To address the challenge, we transform the response variable and establish a new connection between quantile regression and ordinary linear regression. Then, we provide a distributed estimator that is both computationally and communicationally efficient, where only the gradient information is communicated at each iteration. Theoretically, we show that, after a constant number of iterations, the proposed estimator achieves a near-oracle convergence rate without any restriction on the number of machines. Moreover, we establish the theoretical guarantee for the support recovery. The simulation analysis is provided to demonstrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

PDF Details

JMLR Journal 2019 Journal Article

Distributed Inference for Linear Support Vector Machine

  • Xiaozhou Wang
  • Zhuoyi Yang
  • Xi Chen
  • Weidong Liu

The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vast literature on SVM, much less is known about the inferential properties of SVM, especially in a distributed setting. In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM. The proposed estimator is computationally efficient. In particular, it only requires an initial SVM estimator and then successively refines the estimator by solving simple weighted least squares problem. Theoretically, we establish the Bahadur representation of the estimator. Based on the representation, the asymptotic normality is further derived, which shows that the MDL estimator achieves the optimal statistical efficiency, i.e., the same efficiency as the classical linear SVM applying to the entire data set in a single machine setup. Moreover, our asymptotic result avoids the condition on the number of machines or data batches, which is commonly assumed in distributed estimation literature, and allows the case of diverging dimension. We provide simulation studies to demonstrate the performance of the proposed MDL estimator. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

PDF Details