arXiv:2608.21382v1 Announce Type: new
Abstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and whe...
By V. S. Raghu Parupudi
arXiv:2609.24128v1 Announce Type: cross
Abstract: Judge-specific sensitivity is useful for aggregating pairwise LLM evaluations, but its interpretation depends on which systematic presentation effect...
By You Liu, Yue Liu, Quanchao Lu, Nick Shipilov
The study investigates whether small differences in leaderboard rankings between large language models (LLMs) are robust to changes in benchmark composition. Using item‑level responses from five benchmarks and a spectral approximation to multidimensional item‑response theory, the authors find that while overall rankings remain highly correlated, a significant portion (30.9–47.1%) of near‑tie pairs reverse order when benchmark items are recomposed based on low differential item functioning. This suggests that sub‑one‑percentage‑point leaderboard gaps may not reliably reflect true model superiority.
By Qiaoyuan Zheng, Yiqu Yang
arXiv:2606. 13685v1 Announce Type: cross Abstract: LLM-as-a-Judge is now widely used to rank model outputs, train reward models, and populate public leaderboards, but its run-to-run reliability remains under-characterized.
By Abel Yagubyan
arXiv:2608. 15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question.
By Volodymyr Ovcharov
The paper introduces a pluralistic agreement index, Gamma, to quantify how often wrong runs of large language models (LLMs) agree with the majority consensus. By decomposing Gamma into a mechanical component and a preference‑unexplained residual, the authors show that on GPT‑4.1 the mechanical part explains most of the agreement on multiple‑choice benchmarks but only about half on open‑domain tasks, revealing a residual bias that can cause self‑consistency to backfire on hard questions. The study provides a quantitative framework for understanding when majority voting over LLM samples improves or harms accuracy, without proposing new voting methods.
By Lizhuo Zhang, Mengmeng Tang, Chenfeng Long, Xiaoyong Tang, Xiang Luo