DISF

ACL 2026

Background

RAG models suffer faithfulness hallucinations by prioritizing parametric memory over retrieved evidence. Existing detectors trade accuracy for efficiency, and single-pass white-box methods lack contrastive negative signals.

Content

We propose a dual-path internal-state forcing framework that constrains the model to traverse identical response trajectories under context-rich (CTX) and context-free (NOCTX) conditions.

Core Contribution

We operationalize the theoretical necessity of contrastive negative signals. Three feature families—Conflict, Drift, and Instability—capture the latent distributional shifts underlying unfaithful generation.

Experimental Result

DISF outperforms both unsupervised uncertainty methods and supervised internal-state baselines across six backbones and two benchmarks (RAGTruth and HalluRAG), achieving a Pareto improvement between detection accuracy and computational efficiency.

Key Numbers

• 6 backbones, 2 benchmarks
• Pareto improvement: accuracy + efficiency
• 3 feature families: Conflict, Drift, Instability