arXiv AI
Aug 19

Grading Needs a Rubric, Not Intelligence

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 Machine Learning
Aug 4

When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design

arXiv:2608. 01378v1 Announce Type: new Abstract: Design campaigns in chemistry, materials science, and machine learning share a bottleneck: determining how good a candidate truly is requires an expensive evaluation - an experiment, a first-principles simulation, or a full training run.

By Shuangxiu (Max), Ma (Zachary), Wenhe (Zachary), Zhao
arXiv AI
Sep 24

Ask Which, Not How Good: Sizing Benchmarks Scored by an LLM

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