pith:WZNM7LOH
Comparing Developer and LLM Biases in Code Evaluation
LLM judges underperform human annotators by 12-23% when predicting developer code preferences across realistic tasks.
arxiv:2603.24586 v2 · 2026-03-25 · cs.SE · cs.CL
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Claims
Among 13 different models, the best judges underperform human annotators by 12-23%. TRACE identifies 35 significant sources of misalignment between humans and judges across interaction modalities, the majority of which correspond to existing software engineering code quality criteria.
Human preferences collected via annotation are treated as the ground truth without significant bias or inconsistency, and the automatic rubric extraction accurately isolates the sources of misalignment without introducing artifacts from the extraction process itself.
TRACE shows LLM judges underperform human annotators by 12-23% and misalign on 35 code quality dimensions across three coding modalities, with biases often matching existing software engineering criteria.
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| First computed | 2026-05-17T23:38:59.541346Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
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| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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