The paper investigates how benchmark contamination—leakage of test items into training data—affects large language model (LLM) leaderboards. By comparing original test items with semantically equivalent paraphrases, the authors measure contamination as a violation of anchor-item invariance and find that it inflates absolute scores but rarely changes model rankings. Across 47 public models and 74 finetuned models on four benchmarks, the rank correlation between standard and paraphrase-controlled leaderboards is 0.997, with only a handful of cases showing differential contamination that could alter rankings.
By Xingyao Xiao (Stanford University), Yihong Cheng (City University of Macau)
arXiv:2606. 03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks cannot be trusted, their items having likely leaked into pretraining, so scores reflect memorization rather than fitness.
By Alexander Apartsin, Yehudit Aperstein
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:2608. 15980v1 Announce Type: cross Abstract: Preference benchmarks are built by hiring annotators, and the identity of those annotators is treated as an implementation detail.
By Anik Jha
The study analyzes 373,019 judgments from LLM‑scored benchmarks, decomposing variance into system, item, judge, and interaction components via generalizability theory. It finds that with a single judge, generalizability converges to a ceiling determined by the system‑by‑judge variance, which is substantially lower in pairwise preference settings, allowing one judge to suffice. The research also reveals significant biases in presentation order and highlights that many published win‑rate claims fall below the measured floor of the benchmarks.
By Atul Anand
The paper investigates how the order of candidate documents in a prompt affects the decisions made by large‑language‑model (LLM) scorers, even when their ranking quality is similar. It shows that five scorers with only a 0.010 nDCG@10 difference can produce retained‑set overlaps as low as 0.66–0.84, and that existing rerankers still exhibit significant order dependence. The authors propose Order‑Consistency SFT (OC‑SFT), a training method that reduces this dependence, maintaining ranking quality while improving decision stability across multiple tasks.
By Markus Frohmann, Mahdiyar Alavi, Elizabeth Lingg, Navid Rekabsaz
arXiv:2608.22432v1 Announce Type: cross
Abstract: Multilingual LLM judges produce different evaluator-backbone rankings depending on the prompt language: on an eight-language Agent-as-a-Judge benchma...
By Alhasan Mahmood, Samir Abdaljalil, Hasan Kurban
arXiv:2609.39229v1 Announce Type: cross
Abstract: Automatic evaluation of faithfulness increasingly relies on a large language model acting as a judge, yet the most reliable judges are proprietary fr...
By Elia Onofri, Roberto Di Pietro
The paper investigates how compact an encoder can be for closed‑set decision tasks in language‑model pipelines, comparing joint versus per‑option scoring. In a 32‑million‑parameter study, per‑option scoring outperforms joint scoring by 5.8 chance‑corrected skill points on a 37‑benchmark suite, and two external benchmarks corroborate this advantage. Across six released joint encoders ranging from 17 million to 1 billion parameters, the 68‑ and 150‑million‑parameter models surpass a 435‑million‑parameter DeBERTa‑v3 zero‑shot classifier on 209 overlapping tasks and match it on 145 unseen tasks, while the smallest model processes a median request in 14 ms versus 728 ms for the classifier.
By Tardelli Ronan Coelho Stekel
arXiv:2609.00654v1 Announce Type: new
Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
By Qiming Bao, Ne\c{s}et \"Ozkan Tan, Siyuan Wang, Mark Gahegan
A stable compression score can still select the worse model. In our dense study, a split-half reliable path-quadratic score predicted a 16.
MetroLLM-Bench is a 955‑case benchmark designed to evaluate language models as the policy layer of transit kiosks across six real metro systems, covering routing, fare calculation, disruptions, accessibility, and adversarial input. The benchmark includes 14 deterministic scoring components (Tier 1) and 8 semantic‑quality components (Tier 2), with a 75/25 split for training‑data generation and held‑out evaluation. Twenty‑six models from six vendors were tested, and a 4B Qwen 3.5 student fine‑tuned via PEFT outperformed GPT‑5.6 on Tier 1 and matched GPT‑5.4 on the combined score, while larger models offered no further improvement.
By Remco Hendriks (Continker)