The Geometry of Truth: Layer-wise Semantic Dynamics for Hallucination Detection in Large Language Models

• URL: http://arxiv.org/abs/2510.04933v1
• Date: Mon, 06 Oct 2025 15:41:22 GMT
• Title: The Geometry of Truth: Layer-wise Semantic Dynamics for Hallucination Detection in Large Language Models
• Abstract summary: We present Layer-wise Semantic Dynamics, a geometric framework for hallucination detection.<n>LSD operates intrinsically within the model's representational space.<n> evaluated on the TruthfulQA and synthetic factual-hallucination datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) often produce fluent yet factually incorrect statements-a phenomenon known as hallucination-posing serious risks in high-stakes domains. We present Layer-wise Semantic Dynamics (LSD), a geometric framework for hallucination detection that analyzes the evolution of hidden-state semantics across transformer layers. Unlike prior methods that rely on multiple sampling passes or external verification sources, LSD operates intrinsically within the model's representational space. Using margin-based contrastive learning, LSD aligns hidden activations with ground-truth embeddings derived from a factual encoder, revealing a distinct separation in semantic trajectories: factual responses preserve stable alignment, while hallucinations exhibit pronounced semantic drift across depth. Evaluated on the TruthfulQA and synthetic factual-hallucination datasets, LSD achieves an F1-score of 0.92, AUROC of 0.96, and clustering accuracy of 0.89, outperforming SelfCheckGPT and Semantic Entropy baselines while requiring only a single forward pass. This efficiency yields a 5-20x speedup over sampling-based methods without sacrificing precision or interpretability. LSD offers a scalable, model-agnostic mechanism for real-time hallucination monitoring and provides new insights into the geometry of factual consistency within large language models.

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