The Attribution Blind Spot

ARR / EMNLP

Background

When retrieved documents overlap with pretraining data, models can produce faithful-looking text entirely from parametric memory. Output-level monitoring cannot distinguish “read-from-context” from “recalled-from-memory.”

Content

We formalize the “attribution blind spot” and introduce Computational Reality Monitoring (CRM), adapted from cognitive science’s reality-monitoring framework.

Core Contribution

CRM compares internal representations with and without context to detect membership-conditioned representational divergence that output-level monitors systematically miss.

Experimental Result

Output-level signals collapse (likelihood baseline AUC 0.55–0.60), while CRM raises detection to 0.71–0.95 AUC across nine model variants. On BookMIA, AUC reaches 0.84–0.97. The signal collapses on domain-confounded benchmarks, validating boundary conditions.

Key Numbers

• CRM: 0.71–0.95 AUC (vs. 0.55–0.60 baseline)
• BookMIA: 0.84–0.97 AUC
• 9 model variants tested
• Signal collapses on domain-confounded benchmarks (boundary validated)