The study evaluates a label‑free rubric‑clarification procedure that rewrites only the most contested sub‑criteria of a rubric, aiming to reduce disagreement among large‑language‑model (LLM) judges. Across six rubric conditions and three datasets (SummEval, HealthBench, USR Topical‑Chat), the method increased inter‑judge agreement on SummEval by 0.069 Krippendorff’s α, but did not improve agreement with human expert ratings. In other datasets, neither inter‑judge nor human agreement met the preregistered effect criteria, and human agreement was actually lower after editing contested clauses compared to random edits.
By Sangdo Han, Moonsoo Kim
arXiv:2607. 08535v1 Announce Type: cross Abstract: An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed.
By Zongyou Yang, Yinghan Hou, Xiaokun Yang
The paper introduces Conditional Accuracy Profiling (CAP), a diagnostic framework that breaks down LLM‑judge accuracy into eight deployment conditions covering content sensitivity, robustness, and rationale quality. CAP is benchmark‑agnostic and can be applied directly when annotations are available, via task‑subset proxies, or through controlled augmentation. Applied to seven judges across six benchmarks, CAP reveals that aggregate accuracy masks important differences—for example, judges highly sensitive to omitted qualifications rank poorly on a Hard‑Constructed subset, and Position Robustness is generally stable but can degrade sharply under adversarial stress.
By Wenqi Li, Bin Liu, Mindi Ruan, Chuanbo Hu, Minglei Yin, Xin Li
arXiv:2610.05094v1 Announce Type: cross
Abstract: Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design...
By Laur\`ene Vaugrante, Thilo Hagendorff
arXiv:2606. 05384v1 Announce Type: new Abstract: LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators.
By Srimonti Dutta, Akshata Kishore Moharir
The paper investigates whether widely used evaluation frameworks for large language models (LLMs) implement a defense called commit‑first judging, which requires a judge to solve a task itself before accepting a candidate answer. Across 24 configurations in eight popular frameworks, none use the full commit‑first method; nine use a weaker variant that is ineffective. In controlled experiments, the weaker variant allowed systems to game the judge, while the full commit‑first approach eliminated this vulnerability but sometimes worsened evaluation when the judge’s own answer was incorrect.
By Idil Gozel