Evidence map›Paper›PMID 38595796›Full record

ReviewiScience2024

Integration of cognitive tasks into artificial general intelligence test for large models.

Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du and 3 more

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 2 countries.

Youzhi QuDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Chen WeiDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Penghui DuDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Wenxin CheDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Chi ZhangDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Wanli OuyangShanghai AI Laboratory, Shanghai 200232, China.
Yatao BianTencent AI lab, Shenzhen 518057, China.
Feiyang XuiFLYTEK AI Research, Hefei 230088, China.
Bin HuSchool of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Kai DuInstitute for Artificial Intelligence, Peking University, Beijing 100871, China.
Haiyan WuCentre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China.
Jia LiuDepartment of Psychology, Tsinghua University, Beijing 100084, China.
Quanying LiuDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Southern University of Science and Technology · CNBeijing Institute of Technology · CNPeking University · CNShanghai Artificial Intelligence LaboratoryTsinghua University · CNUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Indexed as

Cognitive neuroscienceNeuroscienceResearch methodology social sciencesSocial sciences

Identifiers

PMID38595796
PMCPMC11001637
OpenAlexW4393098213

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.