The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain¶
Claim · All claims
Evidence · 1 study (2 entries) · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? On the 146 pairs where both annotators agree, judge agreement is 87.7% (κ = 0.842; macro-F1 0.841). → Yubo Li 2026
q2 i? Severity agreement is less settled: judge agreement is 65.3% with annotator A (κ = 0.385) but 93.9% with annotator B (κ = 0.827), with medium-tier boundaries particularly uncertain. → Yubo Li 2026 (2)
Evidence¶
Yubo Li 2026¶
Yubo Li, Rema Padman, Ramayya Krishnan. (2026). Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks. arXiv preprint. https://arxiv.org/abs/2607.22606 doi:10.48550/arxiv.2607.22606
q2 · i? · associational · r2
Human-annotation study of 200 pairs stratified 40 per judge label, independently labelled by two annotators with judge outputs hidden. Inter-annotator agreement is 73.0% (κ = 0.655); the article notes the subset score is unsuitable as population accuracy.
"On the 146 pairs where A and B agree, judge agreement is 87.7% (κ= 0.842 ; macro-F1 0.841). Per-label F1 on this subset is 1.00 for ABSENT, 0.83 CONSISTENT, 0.70 COMPLEMENTARY, 0.69 DIVERGENT, and 0.99 CONTRADICTORY."
Yubo Li 2026 (2)¶
Yubo Li, Rema Padman, Ramayya Krishnan. (2026). Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks. arXiv preprint. https://arxiv.org/abs/2607.22606
q2 · i? · associational · r2
Severity-calibration subset of the same 200-pair annotation study, summarized in Table 1 showing medium-tier matches are particularly uncertain. The article therefore uses severity only as a review flag, without treating either annotator as an adjudicated clinical reference.
"Among 49 pairs with shared relationship labels and three available severity ratings, A–B agreement is 67.3% ( κ= 0.425 ); judge agreement is 65.3% with A (κ= 0.385 ) and 93.9% with B (κ= 0.827 )."
Discussion¶
Related Claims¶
- The cross-family LLM judge panel agrees with human domain experts on every judged axis, reaching the expert ceiling on answer-holding
- Automated judging aligns strongly with three domain experts (r = 0.82 for role fidelity) but exhibits a conservative bias, scoring ethical deviation 0.08 points lower than humans
- Within-judge variance of the repeated LLM-judge rubric is negligible, but cross-model-family agreement remains untested
- Human-LLM agreement remains low across constructs (κ = 0.000 to 0.419), with self-efficacy showing the best alignment and Prior KSAs none
- LLMs agree strongly with their own consensus grades (most ICC > 0.8), far more than with human teachers' grades
- Human-LLM agreement on a complex multi-label codebook falls well below human-human agreement, while LLM-LLM agreement is comparable to human-human agreement
- In blind verification, model selection matters more than source type: Bradley-Terry ranking interleaves human coders and LLMs, with per-model win rates spanning significant human advantage to numerical LLM advantage
- Blind expert verification shows no overall preference for human over LLM qualitative coding when sources are judged symmetrically
- Agreement metrics and expert quality judgments diverge in both directions: human consensus can encode shared conservative bias that the verifier rejects in favor of LLM coding
- CLARA achieves the highest agreement with blinded human developmental ranking judgments compared with readability and prompting baselines
- LLM-judge evaluation of tutoring sycophancy shows systematic self-judge blind spots and missed sycophancy even under judge consensus — related
- An automated LLM assessor rates 3E clinical communication skills with agreement comparable to individual human raters — related
- Feedback givers agree on exact urgency tiers only about a fifth of the time, with most disagreements off by a single tier — related
- In Support & Evidence and Claim & Focus, teacher and AES scores agreed at the lowest and highest rubric points but diverged in the middle, with teachers more likely to score a 2 and the AES a 3 — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
judge-calibration-labels-strong-severity-uneven |
title |
The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain |
description |
The LLM judge agrees with human annotators at 87.7% (κ = 0.842) on consensus pairs, but severity-rating agreement is uneven and medium-tier boundaries are uncertain |
status |
review |
evidence_strength |
mixed |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — yubo-li-2026, yubo-li-2026-2 |
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