Claim · 2019-10-23 · publication · Stated in source · Supported

The paper explores transfer learning techniques for NLP by introducing a unified framework that casts every language problem in a text-to-text format.

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

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer → states its contribution → The paper explores transfer learning techniques for NLP by introducing a unified framework that casts every language problem in a text-to-text format.

Where it appears

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

Claim and evidence

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformerstates its contribution ↓The paper explores transfer learning techniques for NLP by introducing a unified framework that casts every language problem in a text-to-text format.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2019-10-23 · publication Records dated 2019 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

arXiv · 2019-10-23

Cite the original source

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

Source excerpt

“In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Colin Raffel; Noam Shazeer; Adam Roberts; Katherine Lee; Sharan Narang; Michael Matena; Yanqi Zhou; Wei Li; Peter J. Liu
Version: arXiv:1910.10683v1, submitted 23 October 2019 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 b41ee513826361ff4d6076ded32e35fa6bc9dea19f6ba2b98feeb1a65c778483). The author list is the byline as printed on the first page.
Version date: 2019-10-23
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”, 2019-10-23.
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 (27 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. 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 paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 23 October 2019 (UTC); later versions may word it differently.

Record ID kp-states-exploring-limits-transfer-learning-unified-2019-1