In the era of noisy intermediate-scale quantum (NISQ) computing, Quantum Neural Networks (QNNs) have emerged as a promising approach for various applications, yet their training is often hindered by barren plateaus (BPs), where gradient variance vanishes exponentially as the model size increases. To address this challenge, we propose a new Large Language Model (LLM)-driven search framework, AdaInit, that iteratively searches for optimal initial parameters of QNNs to maximize gradient variance and therefore mitigate BPs. Unlike conventional one-time initialization methods, AdaInit dynamically refines QNN's initialization using LLMs with adaptive prompting. Theoretical analysis of the Expected Improvement (EI) proves a supremum for the search, ensuring this process can eventually identify the optimal initial parameter of the QNN. Extensive experiments across four public datasets demonstrate that AdaInit significantly enhances QNN's trainability compared to classic initialization methods, validating its effectiveness in mitigating BPs.

With the proliferation of Artificial Intelligence, there has been a massive increase in the amount of data required to be accumulated and disseminated digitally. As the data are available online in digital landscapes with complex and sophisticated infrastructures, it is crucial to implement various defense mechanisms based on cybersecurity. Generative Adversarial Networks (GANs), which are deep learning models, have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. Our survey aims to explore the various works completed in GANs, such as Intrusion Detection Systems (IDS), Mobile and Network Trespass, BotNet Detection, and Malware Detection. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains. Further, the paper discusses the challenges and constraints of using GANs in these areas and suggests future research directions. Overall, the paper highlights the potential of GANs in enhancing cybersecurity measures and addresses the need for further exploration in this field.

The rapid evolution of artificial intelligence (AI) through developments in Large Language Models (LLMs) and Vision-Language Models (VLMs) has brought significant advancements across various technological domains. While these models enhance capabilities in natural language processing and visual interactive tasks, their growing adoption raises critical concerns regarding security and ethical alignment. This survey provides an extensive review of the emerging field of jailbreaking--deliberately circumventing the ethical and operational boundaries of LLMs and VLMs--and the consequent development of defense mechanisms. Our study categorizes jailbreaks into seven distinct types and elaborates on defense strategies that address these vulnerabilities. Through this comprehensive examination, we identify research gaps and propose directions for future studies to enhance the security frameworks of LLMs and VLMs. Our findings underscore the necessity for a unified perspective that integrates both jailbreak strategies and defensive solutions to foster a robust, secure, and reliable environment for the next generation of language models. More details can be found on our website: \url{https://chonghan-chen.com/llm-jailbreak-zoo-survey/}.

Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years. However, this progress has also intensified ethical concerns, impacting the deployment of LLMs in everyday contexts. This paper provides a comprehensive survey of ethical challenges associated with LLMs, from longstanding issues such as copyright infringement, systematic bias, and data privacy, to emerging problems like truthfulness and social norms. We critically analyze existing research aimed at understanding, examining, and mitigating these ethical risks. Our survey underscores integrating ethical standards and societal values into the development of LLMs, thereby guiding the development of responsible and ethically aligned language models.

Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disruptions. Considerable efforts have been devoted to mitigating these challenges. For example, pioneering Bayesian methodologies, including GraphSS and LlnDT, incorporate Bayesian label transitions and topology-based label sampling to strengthen the robustness of GNNs. However, GraphSS is hindered by slow convergence, while LlnDT faces challenges in sparse graphs. To overcome these limitations, we propose a novel label inference framework, TraTopo, which combines topology-driven label propagation, Bayesian label transitions, and link analysis via random walks. TraTopo significantly surpasses its predecessors on sparse graphs by utilizing random walk sampling, specifically targeting isolated nodes for link prediction, thus enhancing its effectiveness in topological sampling contexts. Additionally, TraTopo employs a shortest-path strategy to refine link prediction, thereby reducing predictive overhead and improving label inference accuracy. Empirical evaluations highlight TraTopo's superiority in node classification, significantly exceeding contemporary GCN models in accuracy.

In the era of noisy intermediate-scale quantum (NISQ), variational quantum circuits (VQCs) have been widely applied in various domains, advancing the superiority of quantum circuits against classic models. Similar to classic models, regular VQCs can be optimized by various gradient-based methods. However, the optimization may be initially trapped in barren plateaus or eventually entangled in saddle points during training. These gradient issues can significantly undermine the trainability of VQC. In this work, we propose a strategy that regularizes model parameters with prior knowledge of the train data and Gaussian noise diffusion. We conduct ablation studies to verify the effectiveness of our strategy across four public datasets and demonstrate that our method can improve the trainability of VQCs against the above-mentioned gradient issues.

The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques facilitate the deletion of previously acquired knowledge without requiring extensive model retraining. However, they often overlook a critical issue: unlearning processes bias. This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy. In this work, we analyze the causal factors behind the unlearning process and mitigate biases at both data and algorithmic levels. Typically, we introduce an intervention-based approach, where knowledge to forget is erased with a debiased dataset. Besides, we guide the forgetting procedure by leveraging counterfactual examples, as they maintain semantic data consistency without hurting performance on the remaining dataset. Experimental results demonstrate that our method outperforms existing machine unlearning baselines on evaluation metrics.

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.

Node representation learning by using Graph Neural Networks (GNNs) has been widely explored. However, in recent years, compelling evidence has revealed that GNN-based node representation learning can be substantially deteriorated by delicately-crafted perturbations in a graph structure. To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs. Within these existing works, Bayesian label transition has been proven to be more effective, but this method is extensively reliant on a well-built prior distribution. The variational inference could address this limitation by sampling the latent node embedding from a Gaussian prior distribution. Besides, leveraging the Gaussian distribution (noise) in hidden layers is an appealing strategy to strengthen the robustness of GNNs. However, our experiments indicate that such a strategy can cause over-smoothing issues during node aggregation. In this work, we propose the Graph Variational Diffusion Network (GVDN), a new node encoder that effectively manipulates Gaussian noise to safeguard robustness on perturbed graphs while alleviating over-smoothing issues through two mechanisms: Gaussian diffusion and node embedding propagation. Thanks to these two mechanisms, our model can generate robust node embeddings for recovery. Specifically, we design a retraining mechanism using the generated node embedding to recover the performance of node classifications in the presence of perturbations. The experiments verify the effectiveness of our proposed model across six public datasets.