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Found 8 papers, 6 papers with code
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Answering real-world user queries, such as product search, often requires accurate retrieval of information from semi-structured knowledge bases or databases that involve blend of unstructured (e. g., textual descriptions of products) and structured (e. g., entity relations of products) information.
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts.
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
To bridge this gap, we introduce a new benchmark, namely, the Bias and Interference Challenges in Visual Language Models (Bingo).
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
The objective of GNN explainability is to discern the underlying graph structures that have the most significant impact on model predictions.
Communication-Free Distributed GNN Training with Vertex Cut
Here, we introduce CoFree-GNN, a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training.
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Med-Flamingo: a Multimodal Medical Few-shot Learner
However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time.
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments.
Efficient Automatic Machine Learning via Design Graphs
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.
