A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.
sup ∥w1∥2≤B1 nX i=1 TiX t=1 σi,tw1hi,t # ≤ 2 √ 2B1B2 √ D n Eσ
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Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
A lightweight max-pooling network with MLP detects LLM hallucinations competitively without semantic consistency computations by adaptively aggregating internal token features.