STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases

What: STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases. Given a user query, the task is to extract nodes from the knowledge base that are relevant to the query.

AvaTaR: Optimizing LLM Agents for Tool-Assisted Knowledge Retrieval

GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts

Task: Node and graph classification.
What: A framework that enhances GNN generalization under complex distribution shifts.
Benefits: Generalization and interpretability on distribution shift types.
How: Through a mixture-of-expert architecture and training objective.

Discover and Cure: Concept-aware Mitigation of Spurious Correlation (DISC)

Task: Image classification.
What: An algorithm which adaptively mitigates spurious correlations during model training.
Benefits: Less spurious bias, better generalization, and unambiguous interpretations.
How: Using concept images generated by Stable Diffusion, in each iteration, DISC computes a metric called concept sensitivity to indicate each concept's spuriousness. Guided by it, DISC creates a balanced dataset (where spurious correlations are removed) to update model.

Discovering Invariant Rationales for Graph Neural Networks

Task: Graph classification.
What: An invariant learning algorithm for GNNs.
Motivation: GNNs mostly fail to generalize to OOD datasets and provide interpretations.
Insight: We construct interventional distributions as "multiple eyes" to discover the features that make the label invarian (i.e., causal features).
Benefits: Intrinsic interpretable GNNs that are robust and generalizable to OOD datasets.

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

Task: Graph classification.
What: A graph contrastive learning (GCL) method with model interpretations.
How: We generate rationale-aware graphs for contrastive learning to achieve better transferability.

Knowledge-Aware Meta-learning for Low-Resource Text Classification

Task: Text classification.
What: A meta-learning algorithm for low-resource text classification problem.
How: We extract sentence-specific subgraphs from a knowledge graph for training.
Benefits: Better generalization between meta-training and meta-testing tasks.

Med-Flamingo: a Multimodal Medical Few-shot Learner

Task: Visual question answering, rationale generation etc.
What: A new multimodal few-shot learner specialized for the medical domain.
How: Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks.
Benefits: Few-shot generative medical VQA abilities in the medical domain.

Holistic analysis of hallucination in GPT-4V(ision): Bias and interference challenges (BINGO)

What: A benchmark designed to evaluate two common types of hallucinations in visual language models: bias and interference.

Deconfounding to Explanation Evaluation in Graph Neural Networks

Task: Explanation evaluation.
What: A new paradigm to evaluate GNN explanations.
Motivation: Explanations evaluation fundamentally guides the diretion of GNNs explainability.
Insight: Removal-based evaluation hardly reflects the true importance of explanations.
Benefits: More faithful ranking of different explanations and explanatory methods.

D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion

Task: Explanation generation for GNNs.
What: A unified framework that generates both counterfactual and model-level explanations
Benefits: The explanations are in-distribution and thus more reliable.

Towards Multi-Grained Explainability for Graph Neural Networks

Task: Explanation generation for GNNs.
What: ReFine, a two-step explainer.
How: It generates multi-grained explanations via pre-training and fine-tuning.
Benefits: Obtain both global (for a group) and local explanations (for an instance).

Reinforced Causal Explainer for Graph Neural Networks

Task: Explanation generation for GNNs.
What: Reinforced Causal Explainer (RC-Explainer).
How: It frames an explanatory subgraph via successively adding edges using a policy network.
Benefits: Faithful and concise explanations.