Prompt optimization for language models faces challenges due to the large discrete search space, the reliance on continuous gradient updates, and the need to round continuous representations into discrete prompts, which causes inflexibility and instability.Existing methods attempt to address these by constraining the search space and adopting greedy, incremental improvements, but they often fail to fully leverage historical gradient information.In this paper, we model the prompt optimization problem by the probability distribution of the prompt and present a novel approach that integrates greedy strategies into optimization with continuous representations.This approach can exploit historical gradient information to address the instability caused by rounding in existing methods.Our study indicates that using continuous representations can improve prompt optimization performance on both text classification and attack tasks, as well as models, including GPT-2, OPT, Vicuna, and LLaMA-2, and also be adaptable to models of different sizes.

It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient / scalable modeling of long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations, sensitivity to spurious edges, or inadequate model interpretability. To address these and other issues, two separate strategies have recently been proposed, namely implicit and unfolded GNNs (that we abbreviate to IGNN and UGNN respectively). The former treats node representations as the fixed points of a deep equilibrium model that can efficiently facilitate arbitrary implicit propagation across the graph with a fixed memory footprint. In contrast, the latter involves treating graph propagation as unfolded descent iterations as applied to some graph-regularized energy function. While motivated differently, in this paper we carefully quantify explicit situations where the solutions they produce are equivalent and others where their properties sharply diverge. This includes the analysis of convergence, representational capacity, and interpretability. In support of this analysis, we also provide empirical head-to-head comparisons across multiple synthetic and public real-world node classification benchmarks. These results indicate that while IGNN is substantially more memory-efficient, UGNN models support unique, integrated graph attention mechanisms and propagation rules that can achieve strong node classification accuracy across disparate regimes such as adversarially-perturbed graphs, graphs with heterophily, and graphs involving long-range dependencies.

Recent advances of incorporating layout information, typically bounding box coordinates, into pre-trained language models have achieved significant performance in entity recognition from document images. Using coordinates can easily model the position of each token, but they are sensitive to manipulations in document images (e.g., shifting, rotation or scaling) which are common in real scenarios. Such limitation becomes even worse when the training data is limited in few-shot settings. In this paper, we propose a novel framework, LAGER, which leverages the topological adjacency relationship among the tokens through learning their relative layout information with graph neural networks. Specifically, we consider the tokens in the documents as nodes and formulate the edges based on the topological heuristics. Such adjacency graphs are invariant to affine transformations, making it robust to the common image manipulations. We incorporate these graphs into the pre-trained language model by adding graph neural network layers on top of the language model embeddings. Extensive experiments on two benchmark datasets show that LAGER significantly outperforms strong baselines under different few-shot settings and also demonstrate better robustness to manipulations.

Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independently as input or do not fully utilize the multi-row features and labels to directly change and enhance the target data representations. In this paper, we propose to 1) construct a hypergraph from relevant data instance retrieval to model the cross-row and cross-column patterns of those instances, and 2) perform message Propagation to Enhance the target data instance representation for Tabular prediction tasks. Specifically, our specially-designed message propagation step benefits from 1) the fusion of label and features during propagation, and 2) locality-aware multiplicative high-order interaction between features. Experiments on two important tabular prediction tasks validate the superiority of the proposed PET model against other baselines. Additionally, we demonstrate the effectiveness of the model components and the feature enhancement ability of PET via various ablation studies and visualizations. The code is available at https://github.com/KounianhuaDu/PET.

Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e.g., as can occur as a result of graph heterophily or adversarial attacks. To at least partially address these issues within a simple transparent framework, we consider a new family of GNN layers designed to mimic and integrate the update rules of two classical iterative algorithms, namely, proximal gradient descent and iterative reweighted least squares (IRLS). The former defines an extensible base GNN architecture that is immune to oversmoothing while nonetheless capturing long-range dependencies by allowing arbitrary propagation steps. In contrast, the latter produces a novel attention mechanism that is explicitly anchored to an underlying end-to-end energy function, contributing stability with respect to edge uncertainty. When combined we obtain an extremely simple yet robust model that we evaluate across disparate scenarios including standardized benchmarks, adversarially-perturbated graphs, graphs with heterophily, and graphs involving long-range dependencies. In doing so, we compare against SOTA GNN approaches that have been explicitly designed for the respective task, achieving competitive or superior node classification accuracy. Our code is available at https://github.com/FFTYYY/TWIRLS. And for an extended version of this work, please see https://arxiv.org/abs/2103.06064.

The variety of arc fault induced by different load types makes residential series arc fault detection complicated and challengeable. This paper proposes a hybrid time and frequency analysis and fully connected neural network (HTFNN) based method to identify series ac arc fault. The HTFNN method recognizes samples state by broadly classifying them into resistive (Re) category, capacitive-inductive (CI) category, and switching (Sw) category according to the fundamental frequency components of series current. In each category, separate fully connected neural network (NN) with customized time and frequency indicators being the input is employed for fine class recognition and state identification. This method construction addresses the feature overlap among various cases by separate categories and enables suitable indicator selection within each category. Since the identification complexity in each category is cheaper than identifying all cases together, concise NNs are applied in this paper to reduce computing cost. The general accuracies of normal and arcing state identification in Re, CI, and Sw category are 99.64%, 100%, and 98.45%, respectively, based on 3950 test samples. Method comparison shows that HTFNN achieves higher detection precision at lower computational complexity for generalized load types. This paper also evaluates the feasibility of implementing the method in hardware for arc fault detection device application.