LLM judges exhibit high stability under neutral re-evaluation but substantial reversibility under targeted post-decision challenges, quantified via a new Evaluation Robustness Score (ERS).
Evaluating and mitigating llm-as-a-judge bias in communication systems, 2026
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
An LLM-native five-factor psychometric instrument produces stable self-report structure but fails to predict observed behavior, and reveals a shared textual-surface bias between self-report and LLM judges that human raters do not share.
citing papers explorer
-
Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges
LLM judges exhibit high stability under neutral re-evaluation but substantial reversibility under targeted post-decision challenges, quantified via a new Evaluation Robustness Score (ERS).
-
An LLM-Native Psychometric Instrument Does Not Predict LLM Behavior: Evidence Across 25 Models
An LLM-native five-factor psychometric instrument produces stable self-report structure but fails to predict observed behavior, and reveals a shared textual-surface bias between self-report and LLM judges that human raters do not share.