Qingfeng Chen

Wei Lan, Guohang He, Mingyang Liu et al.

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structure and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks such as batch correction, cell clustering, cell type annotation, gene regulatory network inference and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers interested in the direction of single-cell language models.

Qingfeng Chen, Shiyuan Li, Yixin Liu et al.

Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading to unreliable and risky predictions in real-world scenarios. To bridge the gap, in this paper, we propose a novel Evidence Fusing Graph Neural Network (EFGNN for short) to achieve trustworthy prediction, enhance node classification accuracy, and make explicit the risk of wrong predictions. In particular, we integrate the evidence theory with multi-hop propagation-based GNN architecture to quantify the prediction uncertainty of each node with the consideration of multiple receptive fields. Moreover, a parameter-free cumulative belief fusion (CBF) mechanism is developed to leverage the changes in prediction uncertainty and fuse the evidence to improve the trustworthiness of the final prediction. To effectively optimize the EFGNN model, we carefully design a joint learning objective composed of evidence cross-entropy, dissonance coefficient, and false confident penalty. The experimental results on various datasets and theoretical analyses demonstrate the effectiveness of the proposed model in terms of accuracy and trustworthiness, as well as its robustness to potential attacks. The source code of EFGNN is available at https://github.com/Shiy-Li/EFGNN.

Wei Lan, Wenyi Chen, Qingfeng Chen et al.

The Large Visual Language Models (LVLMs) enhances user interaction and enriches user experience by integrating visual modality on the basis of the Large Language Models (LLMs). It has demonstrated their powerful information processing and generation capabilities. However, the existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. Although lots of work has been devoted to the issue of hallucination mitigation and correction, there are few reviews to summary this issue. In this survey, we first introduce the background of LVLMs and hallucinations. Then, the structure of LVLMs and main causes of hallucination generation are introduced. Further, we summary recent works on hallucination correction and mitigation. In addition, the available hallucination evaluation benchmarks for LVLMs are presented from judgmental and generative perspectives. Finally, we suggest some future research directions to enhance the dependability and utility of LVLMs.