Tag: model interpretability
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Explaining Neural Networks in Preference Learning with ILP
Discover how Inductive Logic Programming (ILP) explains neural networks in preference learning, enhancing interpretability and model fidelity.
Fixing Reasoning Failures in Large Models with StepFlow
Discover how StepFlow improves reasoning accuracy in large models by repairing information flow without retraining, enhancing math, science, and coding tas...
Selective Attention Aggregation Boosts Diffusion Visuals
Enhance diffusion-based visual interpretation with selective aggregation of attention maps, improving accuracy and model transparency in text-to-image gene...
LLM Reasoning Trajectories: Geometry & Accuracy Insights
Explore how LLM reasoning follows step-specific trajectories, enabling accuracy prediction and control through geometric analysis of representations.
Detect Multi-Agent Collusion Using Interpretability Tools
Explore advanced techniques to detect multi-agent collusion through model interpretability and activation analysis in multi-agent systems.