Evidence map›Paper›PMID 39186652›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Language models align with human judgments on key grammatical constructions.

Jennifer Hu, Kyle Mahowald, Gary Lupyan, Anna Ivanova, Roger Levy

Abstract readLetter
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Review
  2. Review
  3. Do Language Models Know Who Did What to Whom?Open mind : discoveries in cognitive science · 2026
    Article
  4. Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026
    Article
  5. A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans.Open mind : discoveries in cognitive science · 2026
    Article
  6. Derivational morphology reveals analogical generalization in large language models.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Reply to Hu et al.: Applying different evaluation standards to humans vs. Large Language Models overestimates AI performance.Proceedings of the National Academy of Sciences of the United States of America · 2024
    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

5 authors.

Jennifer HuKempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02138.
Kyle MahowaldDepartment of Linguistics, The University of Texas at Austin, Austin, TX 78712.
Gary LupyanDepartment of Psychology, University of Wisconsin-Madison, Madison, WI 53706.ORCID 0000-0001-8441-7433
Anna IvanovaSchool of Psychology, Georgia Tech, Atlanta, GA 30332.ORCID 0000-0002-1184-8299
Roger LevyDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-4493-8864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

JudgmentLanguageHumansLinguistics

Identifiers

PMID39186652
PMCPMC11388428

What OpenQuestion holds

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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.