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EMNLP 2026 · Published

Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification

GRD reuses evidence representations while detecting consequential factual differences.

Gated Residual Discrepancy

Similarity is not support

Two sentences can share a topic while differing in a number, entity or condition. Shared context may dominate similarity scores, whereas evidence support is directional.

Retaining differences, filtering background

GRD separately encodes evidence and claims into multiple semantic slots. Directional and magnitude residuals describe their differences; adaptive gating filters background noise before aggregation. Evidence representations can be cached.

Figure 2: GRD encoding, residuals, gating and aggregation
Figure 2: GRD encoding, residuals, gating and aggregation · Source

Reading the efficiency result

Across the reported benchmarks, average Macro-F1 is 79.99%, 1.15 points below the strongest cross-encoder baseline, with a 23.55× speedup. These are experimental comparisons, not deployment guarantees.

Where it fits

Repeated checks against shared evidence are a natural application. Task-specific validation should inspect numbers, negation and qualifiers, alongside aggregate scores.

Paper and resources

Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification

This overview introduces the work; consult the paper for complete methods and experimental settings.