A Novel Differential Feature Learning for Effective Hallucination Detection and Classification

Large language model hallucination represents a critical challenge where outputs deviate from factual accuracy due to distributional biases in training data. While recent investigations establish that specific hidden layers exhibit differences between hallucinatory and factual content, the precise localization of hallucination signals within layers remains unclear, limiting the development of efficient detection methods. We propose a dual-model architecture integrating a Projected Fusion (PF) block for adaptive inter-layer feature weighting and a Differential Feature Learning (DFL) mechanism that identifies discriminative features by computing differences between parallel encoders learning complementary representations from identical inputs. Through systematic experiments across HaluEval's question answering, dialogue, and summarization datasets, we demonstrate that hallucination signals concentrate in highly sparse feature subsets, achieving significant accuracy improvements on question answering and dialogue tasks. Notably, our analysis reveals a hierarchical "funnel pattern" where shallow layers exhibit high feature diversity while deep layers demonstrate concentrated usage, enabling detection performance to be maintained with minimal degradation using only 1% of feature dimensions. These findings suggest that hallucination signals are more concentrated than previously assumed, offering a pathway toward computationally efficient detection systems that could reduce inference costs while maintaining accuracy.