Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry

By identifying interference between salient features, the approach anticipates failure modes in tasks like multihop reasoning and multilingual recall, enabling targeted stress tests and enhancing active learning for real-world applications.

Key Points

• Predicts LLM failure modes using representational geometry without specific input evaluation.
• Identifies interference between features as a cause of compositional failure.
• Demonstrates reliable composition in near-orthogonal concept pairs.
• Applicable to tasks requiring systematic composition like multilingual factual recall.
• Lays groundwork for scalable active learning and stress testing in deployment.