202 citations · 225 across the 11 of their papers we have counts for
11 papers
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following
Jinnan Li, Jinzhe Li, Yue Wang +2
Multi-turn instruction following capability constitutes a core competency of large language models (LLMs) in real-world applications. Existing evaluation benchmarks predominantly f…
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli
Xu Wang, Cheng Li, Yi Chang +2
Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications…
Language Models can Evaluate Themselves via Probability Discrepancy
Tingyu Xia, Bowen Yu, Yuan Wu +2
In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in…
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
Yuan Wu
Multi-domain text classification (MDTC) endeavors to harness available resources from correlated domains to enhance the classification accuracy of the target domain. Presently, mos…
Regularized Conditional Alignment for Multi-Domain Text Classification
Juntao Hu, Yuan Wu
The most successful multi-domain text classification (MDTC) approaches employ the shared-private paradigm to facilitate the enhancement of domain-invariant features through domain-…
A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang +13
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recent…
Maximum Batch Frobenius Norm for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learni…
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to disentangle the explanatory and transferable features for domain adaptation. However, there…
Dual Adversarial Co-Learning for Multi-Domain Text Classification
Yuan Wu, Yuhong Guo
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). The approach learns shared-private networks for feature extracti…
Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network
Yuan Wu, Lingling Li, Lian Li
We introduce the chi-square test neural network: a single hidden layer backpropagation neural network using chi-square test theorem to redefine the cost function and the error func…