LLM Performance on a Real, Double-Marked GCSE Benchmark
A new study introduces a dataset of 32,534 double-marked real student responses to GCSE mock exams, covering five subjects and handwritten work. Top LLMs agree with examiners more closely than examiners agree with each other, handling subjective and handwriting tasks effectively, with little dependence on model size.
[2606.24973] LLM Performance on a Real, Double-Marked GCSE Benchmark
[Submitted on 23 Jun 2026]
Title:LLM Performance on a Real, Double-Marked GCSE Benchmark
View a PDF of the paper titled LLM Performance on a Real, Double-Marked GCSE Benchmark, by Malachy Fox and 2 other authors
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Abstract:We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work. We test whether off-the-shelf large language models agree with examiners as closely as the two examiners agree with each other. We find that models overwhelmingly agree well with the examiner consensus across subjects, with the top performing models agreeing more closely with examiners than examiners agree with each other. Models achieve high scores for subjective tasks like English essay marking, as well as handling complex and messy handwritten Maths paper scripts. Agreement is uniform near the examiner line, and not massively discriminated by model size, providing cost-effective automated marking solutions.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as: arXiv:2606.24973 [cs.CL]
(or arXiv:2606.24973v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.24973
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Paul Jung [view email] [v1] Tue, 23 Jun 2026 11:46:14 UTC (109 KB)
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