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Zhao et al. (2023) cite Hoffmann et al., "Training compute-optimal large language models" (2022), as reference [34], one of four works cited in the sentence that says the research community coins the term "large language models (LLM)" for large-sized pre-trained language models. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source
Date 2023-03-31 · event Records dated 2023 → Scope and limits A citation, not a naming credit: the survey credits the research community and cites four works as examples; it does not say that this paper named the term. Reference [34] is identified by the survey’s discussion of the paper (PDF p. 3) and the order of entries in the text layer. Earlier statistical uses of the phrase are another sense, so the dates do not conflict. A citation is neither agreement nor influence.
The evidence 02 primary source Supports the claim
A Survey of Large Language Models ↗ arXiv · 2023-03-31
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Source excerpt “Thus, the research community coins the term “large language models (LLM)” … for these large-sized PLMs [32–35].” Location in source PDF p. 2, introduction
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors Wayne Xin Zhao; Kun Zhou; Junyi Li; Tianyi Tang Version Retrieved for the candidate pack on 2026-10-29 (SHA-256 d58be5fc39608dc9aec45194c602436d793f2f2bf56f267d0e38d6258a9f7a9e). arXiv:2303.18223v1 PDF; later revisions (many) not substituted Only the first-listed authors are recorded here; the source credits further authors. Canonical address Open the canonical source ↗ Accessed 2026-10-29 Language English (en) Attribution Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, “A Survey of Large Language Models”, 2023-03-31. 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, Kinds of AI and technical history (research/technical-history). The quotation (16 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes, PDF line-break hyphens and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (31 October 2026, item 104) re-read the record’s sources and found that they support what the record states. Its point that the record is a citation given as an example, not a naming credit, was applied at admission. No human editorial review. primary source Supports the claim
A Survey of Large Language Models ↗ arXiv · 2023-03-31
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Paraphrase Reference [34] is the work discussed on p. 3 as the study of "the optimal schedule" among model size, data size and compute; in the reference list it is Hoffmann et al., "Training compute-optimal large language models", abs/2203.15556, 2022.
Location in source PDF p. 3, section 2 and PDF p. 34, references
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors Wayne Xin Zhao; Kun Zhou; Junyi Li; Tianyi Tang Version Retrieved for the candidate pack on 2026-10-29 (SHA-256 d58be5fc39608dc9aec45194c602436d793f2f2bf56f267d0e38d6258a9f7a9e). arXiv:2303.18223v1 PDF; later revisions (many) not substituted Only the first-listed authors are recorded here; the source credits further authors. Canonical address Open the canonical source ↗ Accessed 2026-10-29 Language English (en) Attribution Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, “A Survey of Large Language Models”, 2023-03-31. 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, Kinds of AI and technical history (research/technical-history). The paraphrase is Selekoa’s, written from the text retrieved for the candidate pack on 2026-10-29; it is not a quotation and was not machine-matched. The reference numbers are separated from their entries in the text layer; [34] is identified by the body text and the order of entries. A cross-family machine check (31 October 2026, item 104) re-read the record’s sources and found that they support what the record states. Its point that the record is a citation given as an example, not a naming credit, was applied at admission. 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 submission (an arXiv version or a journal’s received date, as the source states it); not a later revision or the venue’s publication date.
Record ID th-term-llm-zhao-credits-chinchilla