4 papers · 1 filter
TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
Honghua Dong, Jiacheng Yang, Xun Deng +4
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, the…
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
Chuqin Geng, Ziyu Zhao, Zhaoyue Wang +3
Existing rule-based explanations for Graph Neural Networks (GNNs) provide global interpretability but often optimize and assess fidelity in an intermediate, uninterpretable concept…
VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts
Chuqin Geng, Yuhe Jiang, Ziyu Zhao +7
Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correlational signals without directly…
NEUROLOGIC: From Neural Representations to Interpretable Logic Rules
Chuqin Geng, Anqi Xing, Li Zhang +3
Rule-based explanation methods offer rigorous and globally interpretable insights into neural network behavior. However, existing approaches are mostly limited to small fully conne…