pith:AFBU3EZF
HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs
Systematic benchmarking reveals NLI Verification as the most effective method for detecting hallucinations in LLMs at an AUROC of 0.88.
arxiv:2605.02443 v2 · 2026-05-04 · cs.CL
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Claims
NLI Verification achieves the highest overall AUROC of 0.88, while RAV achieves the second-highest AUROC of 0.66. HalluScore achieves a Pearson correlation of r = 0.41 with human expert judgments.
That the 72 configurations, chosen models, and three domains sufficiently represent the full range of hallucination behaviors in instruction-following LLMs and that human judgments provide a stable ground truth for validating the new metric.
HalluScan benchmark tests hallucination detectors on LLMs, identifies NLI Verification as top performer with 0.88 AUROC, and introduces HalluScore (r=0.41 with humans) plus a routing method for 2x cost savings.
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| First computed | 2026-05-25T02:02:16.154597Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
01434d932508cbc2aeb2db576cdcb86c38372400133e99ec29c243388ce2d812
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/AFBU3EZFBDF4FLVS3NLWZXFYNQ \
| jq -c '.canonical_record' \
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Canonical record JSON
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