Language Models are Few-Shot Learners
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- Alec Radford · Amanda Askell · Dario Amodei · Ilya Sutskever · Jack Clark · Jared Kaplan · Jeffrey Wu · Mark Chen · Prafulla Dhariwal · Rewon Child · Sam McCandlish · Scott Gray · Tom Henighan
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- Language Models are Few-Shot Learners Neural Information Processing Systems · published 2020 · Advances in Neural Information Processing Systems 33. Located by the cross-family machine check on 2026-10-30 (item A030; Proceedings heading), at the precision of its source; not read again at admission.
- Language Models are Few-Shot Learners arXiv · published 2020-05-28 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 ab71cfaa5b3b9faff9f0624cdb9ce4fa74d843448f2e7e7343fe926616ab98cf). Submission history as arXiv records it (UTC): v1 28 May 2020; v2 1 June 2020; v3 5 June 2020; v4 22 July 2020. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- Language Models are Few-Shot Learners arXiv · published 2020-05-28 · arXiv:2005.14165v1, submitted 28 May 2020 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 f756a2d3e23c2770a3e8c4354965e98d37411c18aab91556df0c335675dec396). The author list is the byline as printed on the first page.
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- Author: Alec RadfordAlec Radford is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Alec Radford: person page →
- Author: Amanda AskellAmanda Askell is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Amanda Askell: person page →
- Author: Dario AmodeiDario Amodei is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Dario Amodei: person page →
- Author: Ilya SutskeverIlya Sutskever is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Ilya Sutskever: person page →
- Author: Jack ClarkJack Clark is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Jack Clark: person page →
- Author: Jared KaplanJared Kaplan is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Jared Kaplan: person page →
- Author: Jeffrey WuJeffrey Wu is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Jeffrey Wu: person page →
- Author: Mark ChenMark Chen is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Mark Chen: person page →
- Author: Prafulla DhariwalPrafulla Dhariwal is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Prafulla Dhariwal: person page →
- Author: Rewon ChildRewon Child is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Rewon Child: person page →
- Author: Sam McCandlishSam McCandlish is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Sam McCandlish: person page →
- Author: Scott GrayScott Gray is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Scott Gray: person page →
- Author: Tom HenighanTom Henighan is a credited author of Language Models are Few-Shot Learners.2020 · publication · Stated in source · Supported Inspect claim & evidence ↗Tom Henighan: person page →
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Cited by · 17
Recorded documents that explicitly reference this one. A citation to a work does not identify which version was read.
- Learning to summarize from human feedbackLearning to summarize from human feedback cites Language Models are Few-Shot Learners.2020-09-02 · publication · Stated in source · Supported Inspect claim & evidence ↗Learning to summarize from human feedback: document page →
- ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation cites Language Models are Few-Shot Learners.2021-07-05 · publication · Stated in source · Supported Inspect claim & evidence ↗ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation: document page →
- Evaluating Large Language Models Trained on CodeEvaluating Large Language Models Trained on Code (arXiv:2107.03374v1) cites Language Models are Few-Shot Learners.2021-07-07 · publication · Stated in source · Supported Inspect claim & evidence ↗Evaluating Large Language Models Trained on Code: document page →
- Why AI alignment could be hard with modern deep learningWhy AI alignment could be hard with modern deep learning cites Language Models are Few-Shot Learners.2021-09-21 · publication · Stated in source · Supported Inspect claim & evidence ↗Cotra, Why AI alignment could be hard with modern deep learning (2021): document page →
- ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation cites Language Models are Few-Shot Learners.2021-12-23 · publication · Stated in source · Supported Inspect claim & evidence ↗ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation: document page →
- Training language models to follow instructions with human feedbackTraining language models to follow instructions with human feedback cites Language Models are Few-Shot Learners.2022-03-04 · publication · Stated in source · Supported Inspect claim & evidence ↗Training language models to follow instructions with human feedback: document page →
- Training Compute-Optimal Large Language ModelsTraining Compute-Optimal Large Language Models cites Language Models are Few-Shot Learners.2022-03-29 · publication · Stated in source · Supported Inspect claim & evidence ↗Training Compute-Optimal Large Language Models: document page →
- Self-critiquing models for assisting human evaluatorsSelf-critiquing models for assisting human evaluators cites Language Models are Few-Shot Learners.2022-06-12 · publication · Stated in source · Supported Inspect claim & evidence ↗Self-critiquing models for assisting human evaluators: document page →
- Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeoverWithout specific countermeasures, the easiest path to transformative AI likely leads to AI takeover cites Language Models are Few-Shot Learners.2022-07-18 · publication · Stated in source · Supported Inspect claim & evidence ↗Cotra, Without specific countermeasures (2022): document page →
- LLaMA (2023 paper)LLaMA (2023 paper) cites Language Models are Few-Shot Learners.2023-02-27 · publication · Stated in source · Supported Inspect claim & evidence ↗LLaMA (2023 paper): document page →
- GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language ModelsGPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models cites Language Models are Few-Shot Learners.2023-03-17 · publication · Stated in source · Supported Inspect claim & evidence ↗Eloundou et al., GPTs are GPTs (2023): document page →
- Qwen Technical ReportQwen Technical Report cites Language Models are Few-Shot Learners.2023-09-28 · publication · Stated in source · Supported Inspect claim & evidence ↗Qwen Technical Report: document page →
- Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionWeak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites Language Models are Few-Shot Learners.2023-12-14 · publication · Stated in source · Supported Inspect claim & evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision: document page →
- Gemini: A Family of Highly Capable Multimodal ModelsGemini: A Family of Highly Capable Multimodal Models cites Language Models are Few-Shot Learners.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 Language Models are Few-Shot Learners.2024-06-18 · publication · Stated in source · Supported Inspect claim & evidence ↗ChatGLM research history and techniques: document page →
- Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement LearningSeed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning cites Language Models are Few-Shot Learners.2025-04-10 · publication · Stated in source · Supported Inspect claim & evidence ↗Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning: document page →
- GPT-4 System CardGPT-4 System Card cites Language Models are Few-Shot Learners.Date unknown · Stated in source · Supported Inspect claim & evidence ↗GPT-4 System Card: document page →
Reports using a method from · 1
Methods this document reports using in its own study. Reported use is not independently verified implementation.
- Language Models are Unsupervised Multitask LearnersBrown et al. (arXiv v1, 2020) report using the same model and architecture as GPT-2, with alternating dense and locally banded sparse attention.2020-05-28 · event · Stated in source · Supported Inspect claim & evidence ↗Language Models are Unsupervised Multitask Learners: document page →
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- In its own wordsThe authors state that scaling up language models greatly improves task-agnostic few-shot performance, in some cases reaching competitiveness with what they call prior state-of-the-art fine-tuning approaches.“Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art finetuning approaches.” arXiv v1, PDF p. 1, abstract2020-05-28 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own words · includes its own priority claimThe paper describes training GPT-3, an autoregressive language model with 175 billion parameters, which it states is ten times larger than any previous non-sparse language model, and testing it in the few-shot setting.“Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.” arXiv v1, PDF p. 1, abstract2020-05-28 · publication · Stated in source · Supported Inspect claim & evidence ↗