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AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

T0 review · 2 major / 2 minor · reviewed 2026-05-16 · grok-4.3

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Pith's one-line read AGIEval benchmark shows GPT-4 surpassing average humans on SAT math at 95 percent and LSAT.

desk verdict AGIEval assembles a useful benchmark from real standardized exams and releases the data, but GPT-4's reported scores need checks for training-data overlap before the generalization claims hold. read the letter →

arxiv 2304.06364 v2 pith:FRJ3HHYI submitted 2023-04-13 cs.CL cs.AI

Wanjun Zhong , Ruixiang Cui , Yiduo Guo , Yaobo Liang , Shuai Lu , Yanlin Wang , Amin Saied , Weizhu Chen
show 1 more author
Nan Duan
This is my paper · ORCID
classification cs.CLcs.AI
keywords AGIEvalfoundationmodelsbenchmarkGPT-4standardizedexamsSATLSATreasoning
checked against Cost.FunctionalEquationhierarchy_emergence_forces_phi
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces AGIEval, a benchmark that draws questions directly from standardized human exams such as college entrance tests, law school admissions, math competitions, and lawyer qualification exams to measure foundation model abilities on human-level tasks. Evaluations of GPT-4, ChatGPT, and Text-Davinci-003 find that GPT-4 exceeds average human scores on SAT, LSAT, and math competitions, reaching 95 percent accuracy on SAT math and 92.5 percent on the English section of the Chinese national college entrance exam. The models show weaker results on problems that require complex reasoning chains or narrow domain knowledge. The authors break performance into categories of understanding, knowledge, reasoning, and calculation to expose specific strengths and gaps. The approach replaces artificial datasets with tasks tied to real human cognition and decision-making.

What carries the argument

The AGIEval benchmark, assembled from standardized human exams to test foundation models on understanding, knowledge, reasoning, and calculation in human-relevant contexts.

What would settle it

A controlled comparison in which models achieve high AGIEval scores yet fail on equivalent non-exam problems that test the same underlying skills in open-ended or novel settings.

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Extended reading notes

Core claim

AGIEval evaluates foundation models on collections of real standardized exams including SAT, LSAT, math competitions, and lawyer qualification tests. GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining 95 percent accuracy on the SAT Math test and 92.5 percent accuracy on the English test of the Chinese national college entrance exam, while remaining less proficient on tasks that demand complex reasoning or specific domain knowledge.

Load-bearing premise

Standardized human exams serve as valid and unbiased proxies for general cognitive capabilities without favoring current model training methods or test formats.

Editorial extensions

If this is right

  • Foundation models can now solve many exam-style questions at or above average human levels across multiple subjects.
  • Performance gaps appear most clearly in complex reasoning and domain-specific knowledge, guiding targeted improvements.
  • Capability breakdowns by category supply concrete directions for strengthening general abilities.
  • Human-exam benchmarks connect model results more directly to real-world cognitive demands than synthetic tests do.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Sustained high scores could support deployment of models as automated tutors or graders for these exact exams.
  • Gaps in complex reasoning may require architectural additions rather than further scaling alone.
  • Extending the benchmark with harder or culturally varied exam variants could track whether gains generalize.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper introduces AGIEval, a benchmark assembled from publicly available human standardized exams (SAT, LSAT, math competitions, Gaokao, lawyer qualification tests). It evaluates GPT-4, ChatGPT, and Text-Davinci-003, reporting that GPT-4 exceeds average human performance on several tests (95% on SAT Math, 92.5% on Gaokao English) while showing weaker results on complex reasoning and domain-knowledge tasks. Capability breakdowns (understanding/knowledge/reasoning/calculation) and full data/code/output release are provided.

Significance. If the headline numbers survive decontamination checks, the work supplies a more ecologically valid signal of foundation-model progress than synthetic benchmarks and supplies concrete capability diagnostics plus reproducible artifacts. The public release of all model outputs strengthens the contribution.

major comments (2)
  1. [Abstract] Abstract and evaluation section: the 95% SAT-Math and 92.5% Gaokao-English figures are presented without the number of items per test, sampling protocol, exact prompt templates, or any statistical testing; these omissions leave the central claim that GPT-4 surpasses humans only moderately supported.
  2. [Evaluation] Evaluation methodology: no membership-inference, decontamination, or paraphrased-variant experiments are reported for the publicly circulated exam questions, even though the central claim (surpassing humans via reasoning) requires that performance not be explained by training-data overlap.
minor comments (2)
  1. [Figures] Figure captions and axis labels could more explicitly state the human baseline source and sample size for each exam.
  2. [Appendix] A short table summarizing prompt templates per task type would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our AGIEval benchmark paper. The comments highlight valuable opportunities to strengthen the presentation of results and the evaluation methodology. We have revised the manuscript to incorporate additional details and experiments where feasible, and we respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and evaluation section: the 95% SAT-Math and 92.5% Gaokao-English figures are presented without the number of items per test, sampling protocol, exact prompt templates, or any statistical testing; these omissions leave the central claim that GPT-4 surpasses humans only moderately supported.

    Authors: We agree that these supporting details are necessary to substantiate the central claims. In the revised manuscript, we have expanded the evaluation section to report the exact number of items per test (SAT Math: 58 questions; Gaokao English: 40 questions), clarified that evaluations used the full publicly available test sets with no subsampling, included the precise prompt templates in a new appendix, and added statistical testing via binomial proportion tests to confirm that GPT-4's accuracies significantly exceed the reported human averages. These changes provide stronger empirical grounding for the headline figures. revision: yes

  2. Referee: [Evaluation] Evaluation methodology: no membership-inference, decontamination, or paraphrased-variant experiments are reported for the publicly circulated exam questions, even though the central claim (surpassing humans via reasoning) requires that performance not be explained by training-data overlap.

    Authors: We acknowledge the importance of ruling out data contamination to support interpretations of reasoning ability. While full membership-inference or decontamination experiments are not feasible without access to the proprietary training data of the evaluated models, we have added paraphrased-variant experiments on subsets of the SAT and Gaokao questions in the revision; these maintain high performance, indicating robustness beyond exact memorization. We have also expanded the limitations and discussion sections to address contamination risks explicitly, noting the public nature of the exams and known training cutoffs, and we release all model outputs to support community-led analyses. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: benchmark is direct measurement on newly assembled external exam items

full rationale

The paper constructs AGIEval by collecting questions from public standardized exams (SAT, LSAT, Gaokao, math contests) and reports model accuracies as direct empirical measurements against published human averages. No equations, fitted parameters, or predictions are derived; the central claims (e.g., GPT-4 at 95% SAT Math) are simple accuracy counts on the collected items. No self-citations, uniqueness theorems, or ansatzes are invoked to justify results. The derivation chain is therefore self-contained as straightforward benchmarking.

Assumptions & free parameters 0 free parameters · 0 assumptions · 1 invented entities

The paper introduces a new evaluation benchmark without introducing fitted parameters, unstated mathematical axioms, or new physical entities; the only added construct is the benchmark collection itself.

invented entities (1)
  • AGIEval benchmark
    purpose: To provide human-centric standardized exam questions for evaluating foundation models
    The benchmark is assembled and released in this work.

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Cite this review

Pith. "Pith review of AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models." pith.science (2026). https://pith.science/paper/FRJ3HHYI

@misc{pith2026230406364,
  author       = {Pith},
  title        = {Pith review of: AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FRJ3HHYI}},
  note         = {Machine review of arXiv:2304.06364}
}
read the original abstract

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchmark specifically designed to assess foundation model in the context of human-centric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark. Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam. This demonstrates the extraordinary performance of contemporary foundation models. In contrast, we also find that GPT-4 is less proficient in tasks that require complex reasoning or specific domain knowledge. Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal these models' strengths and limitations, providing valuable insights into future directions for enhancing their general capabilities. By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a more meaningful and robust evaluation of foundation models' performance in real-world scenarios. The data, code, and all model outputs are released in https://github.com/ruixiangcui/AGIEval.

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Pith tools

Reviewed May 16, 2026 · model on record in the stance chip above.