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pith:AFBU3EZF

pith:2026:AFBU3EZFBDF4FLVS3NLWZXFYNQ
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HalluScan: A Systematic Benchmark for Detecting and Mitigating Hallucinations in Instruction-Following LLMs

Ahmed Cherif

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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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Receipt and verification
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

Aliases

arxiv: 2605.02443 · arxiv_version: 2605.02443v2 · doi: 10.48550/arxiv.2605.02443 · pith_short_12: AFBU3EZFBDF4 · pith_short_16: AFBU3EZFBDF4FLVS · pith_short_8: AFBU3EZF
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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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