Pith Number

pith:FRJ3HHYI

pith:2023:FRJ3HHYIWDAMXDDWYCCHHFAO6Q

not attested not anchored not stored refs resolved

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Amin Saied, Nan Duan, Ruixiang Cui, Shuai Lu, Wanjun Zhong, Weizhu Chen, Yanlin Wang, Yaobo Liang, Yiduo Guo

AGIEval benchmark shows GPT-4 surpassing average humans on SAT math at 95 percent and LSAT.

arxiv:2304.06364 v2 · 2023-04-13 · cs.CL · cs.AI

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\pithnumber{FRJ3HHYIWDAMXDDWYCCHHFAO6Q}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That standardized human exams are valid and representative proxies for general human-level cognitive capabilities without significant selection bias or format advantages for current model architectures.

C3one line summary

AGIEval shows GPT-4 exceeding average human scores on SAT Math at 95% and Chinese college entrance English at 92.5%, while revealing weaker results on complex reasoning tasks.

References

287 extracted · 287 resolved · 12 Pith anchors

[1] Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 · doi:10.24963/ijcai.2022/629

[2] Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages=

[3] 2023 , publisher = 2023

[4] Communications of the ACM , volume= 2021

[5] Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Formal links

2 machine-checked theorem links

Cited by

41 papers in Pith

Baichuan 2: Open Large-scale Language Models

Mistral 7B

Mixtral of Experts

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio

Receipt and verification

First computed	2026-05-17T23:38:48.256487Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2c53b39f08b0c0cb8c76c08473940ef436e2b446e9800fbd3d8aa67c194021d3

Aliases

arxiv: 2304.06364 · arxiv_version: 2304.06364v2 · doi: 10.48550/arxiv.2304.06364 · pith_short_12: FRJ3HHYIWDAM · pith_short_16: FRJ3HHYIWDAMXDDW · pith_short_8: FRJ3HHYI

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FRJ3HHYIWDAMXDDWYCCHHFAO6Q \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2c53b39f08b0c0cb8c76c08473940ef436e2b446e9800fbd3d8aa67c194021d3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ec90ca1ff59448b9db5895e41cc3b23fbad0354bb6abf73b36ab1cb6cf7a9874",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2023-04-13T09:39:30Z",
    "title_canon_sha256": "7f2904bd4ffe5ce96b3b972e1fc8a8968dd88d028084d4c7c8955b278e7f449e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2304.06364",
    "kind": "arxiv",
    "version": 2
  }
}