Claim · 2023-03-17 · publication · Stated in source · Supported

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models cites Scaling Laws for Neural Language Models.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models → cites → Scaling Laws for Neural Language Models

Where it appears

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Claim and evidence

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscites ↓Scaling Laws for Neural Language Models

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2023-03-17 · publication Records dated 2023 →

Scope and limitsAn explicit reference only; neither agreement nor influence. The citing text does not identify which version of the work was read.

The evidence 01

primary sourceSupports the claim

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

arXiv:2303.10130v1 · 2023-03-17

Cite the original source

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Paraphrase

The text gives the arXiv identifier 2001.08361.

Location in source PDF p. 33, reference by arXiv identifier

Machine-checked · 2026-10-31

Source details and check notes

Authors: Tyna Eloundou; Sam Manning; Pamela Mishkin; Daniel Rock
Version: Full text (v1). Current copy retrieved directly on 2026-10-09; no archive capture (Wayback unreachable), so earlier bytes are not authenticated. SHA-256 f3c91bc909258b4824e2ea101a4f489aaea75d422adaf8508a79883b0d0f74bd. Journal version not retrieved. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308. https://doi.org/10.1126/science.adj0998
Canonical address: Open the canonical source ↗
Accessed: 2026-10-09
Language: English (en)
Attribution: Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, arXiv:2303.10130v1, 2023-03-17.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The citation was matched (arXiv identifier) in the text retrieved for the candidate pack on 2026-10-28 and re-found by its mechanical check. The paraphrase is Selekoa’s. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. An explicit reference to a work. Citation alone is neither agreement nor causal influence.

Date. Date of the citing source as recorded in the dataset; unknown where the dataset has none.

Record ID kp-cites-eloundou-et-al-2023-gpts-are-gpts-scaling-laws