nearest-neighbor

ASK-NN is an asymmetric nearest-neighbor test for detecting distribution drifts between reference and query samples, with applications to LLM hallucination detection and artificial-text detection. It is computationally efficient, has theoretical guarantees, and performs competitively against baselines on synthetic and real-world benchmarks.

This paper introduces Layer-wise Representation Dynamics (LRD), a framework with three measurement families to analyze how hidden states change across layers in language models. Applied to 31 models on 30 MTEB tasks, LRD reveals architectural differences and enables label-free model selection and inference-time layer pruning.