The paper introduces a penalty‑aware evaluation framework for Retrieval‑Augmented Generation (RAG) systems that uses asymmetric scoring, knowledge‑gap canaries, and a failure‑attribution pipeline. Applying this framework to three commercial RAG products and a baseline on SimpleQA‑Verified, the authors find that while overall accuracy is similar across systems, canary violation rates vary dramatically, showing that systems differ more in when they answer than in what they answer. The study demonstrates that penalty‑aware scoring can reorder system rankings and is robust across different penalty settings.
By Alden Do Rosario, Hussein Younes, Felipe Pires
arXiv:2609.09372v1 Announce Type: cross
Abstract: Although MMLU is widely adopted as a benchmark for calibrating general AI capabilities, we psychometrically demonstrate that its aggregate score prim...
By Dana Paquin, Riddhiman Jain
The paper investigates how different scoring schemes affect the evaluation of multiple-choice question answering (MCQA) models. It introduces six education-inspired scoring methods that assess abilities such as distractor elimination, abstention, confidence calibration, and self-correction. Experiments on large language models show that these alternative schemes can change model rankings, better predict user preferences, and reveal distinct capabilities compared to traditional accuracy scoring.
By Nishant Balepur, Paiheng Xu, Wei Ai, Eunsol Choi, Rachel Rudinger, Jordan Boyd-Graber
arXiv:2608. 06609v1 Announce Type: new Abstract: Automated item evaluation (AIE) refers to the use of computational methods to assess item quality without requiring manual expert review or field testing of the items under evaluation.
By Hotaka Maeda, Yikai Lu
arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
By Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor
Small language models can grade open‑ended exam answers as reliably as much larger models when they use an explicit rubric. In experiments with six cost‑efficient model configurations, the rubric decouples grading from judge intelligence, with answer identity explaining 95.6% of score variance and judge identity only 0.2%. Removing rubric criteria or the official answer collapses reliability and inflates scores, showing the rubric’s essential role.
By Jhen-Ke Lin
arXiv:2608.20385v1 Announce Type: new
Abstract: Systematic reviews rely on quality appraisal of included studies, a process that is time-consuming and sensitive to ambiguity in checklist criteria. Al...
By Timo van der Kuil (Methodology and Statistics Utrecht University), Bruno Messina Coimbra (Methodology and Statistics Utrecht University), Mirjam van Zuiden (Clinical Psychology Utrecht University), Robert A. Bagheri (Methodology and Statistics Utrecht University), Rens van de Schoot (Methodology and Statistics Utrecht University), Klaas Dieleman (Methodology and Statistics Utrecht University), Berend Greijn (Methodology and Statistics Utrecht University), Stefan Houkes (Methodology and Statistics Utrecht University), Sebastiaan Rodenhuis (Methodology and Statistics Utrecht University), Elizabeth M. Grandfield (Methodology and Statistics Utrecht University)
arXiv:2606. 23915v1 Announce Type: cross Abstract: Practice often treats automatic metrics for attribution in LLM retrieval-augmented generation as interchangeable.
By Tianyu Ding, Aditya Nannapaneni, Juan Pablo De la Cruz Weinstein
The paper investigates when it is better to return an existing draft answer or revise it using retrieved evidence in retrieval‑augmented QA systems. By grading both the draft and its candidate revision with the same correctness judge, the authors define a paired effect called recoverability and train policies to predict it before revision. Experiments on 25,870 open‑domain questions show that a recoverability‑based scorer outperforms a draft‑correctness scorer across multiple Llama setups, improving accuracy–revision trade‑offs and closing a significant portion of the oracle gap, though it still applies harmful revisions in a substantial fraction of cases.
By Nicholas Kashani Motlagh, Tim Anderson, Jeremy Gwinnup, Grant Erdmann
arXiv:2609.37647v1 Announce Type: cross
Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
By Tobias Deu{\ss}er, Lorenz Sparrenberg, Rafet Sifa
arXiv:2608.23411v1 Announce Type: new
Abstract: LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes tha...
By Andrei Chetvergov, Stepan Ukolov, Timofei Sivoraksha, Alexander Evseev, Danil Sazanakov, Mikhail Solovev, Sergey Bolovtsov
arXiv:2608. 11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, which makes them unreliable measures of model knowledge.
By Karl Hanna, Chen Feng