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Found 5 papers, 2 papers with code
Large Language Models Are Unconscious of Unreasonability in Math Problems
Large language models (LLMs) demonstrate substantial capabilities in solving math problems.
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs' responses in an aligned, customizable and explainable manner.
Rethinking Node-wise Propagation for Large-scale Graph Learning
However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required.
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds
Attributed to the unstructured and sparse nature of point clouds, the transformer shows greater potential in point clouds data processing.
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation
Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics.
