Measuring Massive Multitask Language Understanding
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- Authors credited
- Collin Burns · Dan Hendrycks · Jacob Steinhardt
- Dates
- Published 2020-09-07 authorship evidence authorship evidence authorship evidence
- Exact source
- Measuring Massive Multitask Language Understanding arXiv · published 2020-09-07 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 dee2e791d282e4133a216a7ace2f9a9a2572acf59a24433fe976506455c1a3df). Submission history as arXiv records it (UTC): v1 7 September 2020; v2 21 September 2020; v3 12 January 2021. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- Measuring Massive Multitask Language Understanding arXiv · published 2020-09-07 · arXiv:2009.03300v1, submitted 7 September 2020 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 5a4759cf69b9b4c313d73738c25a96699e4ba5c895c713702d84207f8986e703). The author list is the byline as printed on the first page.
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Issue and authorship records
- Author: Collin BurnsCollin Burns is a credited author of Measuring Massive Multitask Language Understanding.2020-09-07 · publication · Stated in source · Supported Inspect claim & evidence ↗Collin Burns: person page →
- Author: Dan HendrycksDan Hendrycks is a credited author of Measuring Massive Multitask Language Understanding.2020-09-07 · publication · Stated in source · Supported Inspect claim & evidence ↗Dan Hendrycks: person page →
- Author: Jacob SteinhardtJacob Steinhardt is a credited author of Measuring Massive Multitask Language Understanding.2020-09-07 · publication · Stated in source · Supported Inspect claim & evidence ↗Jacob Steinhardt: person page →
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Cited by · 14
Recorded documents that explicitly reference this one. A citation to a work does not identify which version was read.
- Training Compute-Optimal Large Language ModelsTraining Compute-Optimal Large Language Models cites Measuring Massive Multitask Language Understanding.2022-03-29 · publication · Stated in source · Supported Inspect claim & evidence ↗Training Compute-Optimal Large Language Models: document page →
- LLaMA (2023 paper)LLaMA (2023 paper) cites Measuring Massive Multitask Language Understanding.2023-02-27 · publication · Stated in source · Supported Inspect claim & evidence ↗LLaMA (2023 paper): document page →
- Qwen Technical ReportQwen Technical Report cites Measuring Massive Multitask Language Understanding.2023-09-28 · publication · Stated in source · Supported Inspect claim & evidence ↗Qwen Technical Report: document page →
- Gemini — Google DeepMind research introduction (2023)Gemini — Google DeepMind research introduction (2023) cites Measuring Massive Multitask Language Understanding.2023-12-06 · publication · Stated in source · Supported Inspect claim & evidence ↗Gemini — Google DeepMind research introduction (2023): document page →
- Gemini: A Family of Highly Capable Multimodal ModelsGemini: A Family of Highly Capable Multimodal Models cites Measuring Massive Multitask Language Understanding.2023-12-19 · publication · Stated in source · Supported Inspect claim & evidence ↗Gemini: A Family of Highly Capable Multimodal Models: document page →
- ChatGLM research history and techniquesChatGLM research history and techniques cites Measuring Massive Multitask Language Understanding.2024-06-18 · publication · Stated in source · Supported Inspect claim & evidence ↗ChatGLM research history and techniques: document page →
- DeepSeek-V3 Technical ReportDeepSeek-V3 Technical Report cites Measuring Massive Multitask Language Understanding.2024-12-27 · publication · Stated in source · Supported Inspect claim & evidence ↗DeepSeek-V3 Technical Report: document page →
- NIST AI 800-1 (second public draft): Managing Misuse Risk for Dual-Use Foundation ModelsNIST AI 800-1 (second public draft): Managing Misuse Risk for Dual-Use Foundation Models cites Measuring Massive Multitask Language Understanding.2025-01 · publication · Stated in source · Supported Inspect claim & evidence ↗NIST AI 800-1, second public draft (2025): document page →
- DeepSeek-R1 research reportDeepSeek-R1 research report cites Measuring Massive Multitask Language Understanding.2025-01-22 · publication · Stated in source · Supported Inspect claim & evidence ↗DeepSeek-R1 research report: document page →
- Seed-X: Building Strong Multilingual Translation LLM with 7B ParametersSeed-X: Building Strong Multilingual Translation LLM with 7B Parameters cites Measuring Massive Multitask Language Understanding.2025-07-18 · publication · Stated in source · Supported Inspect claim & evidence ↗Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters: document page →
- Kimi K2: Open Agentic IntelligenceKimi K2: Open Agentic Intelligence cites Measuring Massive Multitask Language Understanding.2025-07-28 · publication · Stated in source · Supported Inspect claim & evidence ↗Kimi K2: Open Agentic Intelligence: document page →
- ERNIE 5.0 Technical ReportERNIE 5.0 Technical Report cites Measuring Massive Multitask Language Understanding.2026-02-04 · publication · Stated in source · Supported Inspect claim & evidence ↗ERNIE 5.0 Technical Report: document page →
- Seed1.8 Model Card: Towards Generalized Real-World AgencySeed1.8 Model Card: Towards Generalized Real-World Agency cites Measuring Massive Multitask Language Understanding.2026-03-21 · publication · Stated in source · Supported Inspect claim & evidence ↗Seed1.8 Model Card: Towards Generalized Real-World Agency: document page →
- Claude 2 model cardClaude 2 model card cites Measuring Massive Multitask Language Understanding.Date unknown · Stated in source · Supported Inspect claim & evidence ↗Claude 2 model card: document page →
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What the document says it adds
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- In its own wordsThe paper proposes a test intended to measure the multitask accuracy of text models.“We propose a new test to measure a text model’s multitask accuracy.” arXiv v1, PDF p. 1, abstract2020-09-07 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe paper states that the test covers 57 tasks, including elementary mathematics, US history, computer science and law.“The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.” arXiv v1, PDF p. 1, abstract2020-09-07 · publication · Stated in source · Supported Inspect claim & evidence ↗