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

Eloundou et al. (2023) cite the GPT-4 technical report (OpenAI, 2023b in their reference list) for the claim that GPT-4 is an effective discriminator, in the section introducing their use of GPT-4 to rate task exposure.

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 → attributes to → GPT-4 Technical Report

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelsattributes to ↓GPT-4 Technical Report

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2023-03-17 · publication Records dated 2023 →
Kind of attribution: Builds on the work

Scope and limitsThe citing text's attribution; some of its authors are OpenAI authors, as is the cited report. Not independent evidence of GPT-4's classification ability or of the validity of the exposure ratings.

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

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“Recent research indicates that GPT-4 serves as an effective discriminator, capable of applying intricate taxonomies and responding to changes in wording and emphasis. (OpenAI, 2023b)”

Location in source PDF p. 9, §3.4.2 Measuring GPTs with GPT-4

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 quotation (25 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. A cross-family machine check (2026-10-30, item B014) found the attribution at its locator and in the citing text’s reference entry. 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. A document expressly attributes an argument, concept, term, finding or approach to another work, in its own words; the kind of attribution is recorded. The record is the citing document’s claim. It is not evidence that the cited work originated the idea, that the attribution is accurate, or of influence beyond the passage. Citation alone is recorded as a citation.

Date. Date of the citing text as the dataset records it (17 March 2023).

Record ID kp-shaping-eloundou-gpt4