The paper investigates how well large language models (LLMs) can serve as judges in summarization evaluation by applying psychometric techniques. Using Many‑Facet Rasch Models, the authors decompose human and LLM ratings into latent summary quality, rater severity, dimension severity, and rating‑scale thresholds, and introduce a residual hardness metric to capture judging difficulty. Their analysis of 17 open‑weight LLM judges on the SummEval dataset reveals that moderate alignment in latent quality does not translate to alignment in residual hardness; humans and LLMs differ in which summary–dimension units are hard, with LLMs tending to find consistency hard and humans tending to find coherence hard, and some hard cases can be predicted from observable source–summary properties.
Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate agreement fails to reveal whether humans and LLMs...
arXiv:2609.13824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to evaluate the responses of other language models. This approach, known as LLM-as-a-Judge, is faste...
The paper discusses how large language models (LLMs) are used in various evaluation roles—examining benchmarks, judging other models, and rating human content—and frames each as a measurement problem. It proposes using Rasch measurement theory (RMT) to decompose ordinal ratings into distinct facets on a common scale, offering diagnostics for miscalibration and rater bias. A case study applying RMT to the Measuring Hate Speech corpus reveals systematic differences between LLMs and human raters in severity, calibration, robustness, sensitivity, and scale use, suggesting RMT should be part of the evaluation toolkit for LLMs in all roles.
arXiv:2601.08654v3 Announce Type: replace Abstract: Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the...
arXiv:2608. 02455v1 Announce Type: new Abstract: Human-centered assessment tasks, which are essential for systematic decision-making, rely heavily on human judgment and typically lack verifiable ground truth.
arXiv:2605.28313v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in tasks related to reasoning and judgment. However, assessing the quality o...
The paper investigates whether consensus among large language model (LLM) judges truly reflects human alignment. By treating each judge’s scores as vectors, the authors measure spread, effective rank, and angles to human scores across 42 judges on Indic benchmarks, revealing that inter‑judge agreement often mirrors shared blind spots rather than human judgments. They find that while judges agree as much as humans, they only reach 58‑66% of human agreement and frequently focus on axes humans do not weight, indicating that ensemble agreement alone is insufficient evidence of alignment.
arXiv:2608.29517v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as essay graders in learning analytics, evaluated almost exclusively with agreement statistics. Educ...
arXiv:2609.21277v1 Announce Type: cross Abstract: How many human judgments does a panel of language models represent? The answer depends on what is matched. We audit categorical judge panels against...
arXiv:2606. 05170v1 Announce Type: new Abstract: At matched accuracy, open-weight LLMs differ substantially in the shape of their error severity distribution -- a difference invisible to the scalar error rate.
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.
arXiv:2606. 09843v3 Announce Type: replace-cross Abstract: Large language models (LLMs) give stable answers to personality questionnaires, yet these self-reports fail to predict how the models behave.