Language Models are Few-Shot Learners

E457860

research paper scientific article

"Language Models are Few-Shot Learners" is a landmark research paper that demonstrated large-scale transformer-based language models can perform diverse tasks from just a few examples without task-specific training.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (4)

Label	Occurrences
Language Models are Few-Shot Learners canonical	5
GPT-3: Language Models are Few-Shot Learners	4
"Language Models are Few-Shot Learners"	2
paper "Language Models are Few-Shot Learners"	1

How this entity was disambiguated

This entity first appeared as the object of triple T4651147 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Language Models are Few-Shot Learners
Context triple: [Tom B. Brown, notableWork, Language Models are Few-Shot Learners]

A. Language Models are Unsupervised Multitask Learners
"Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
B. Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
C. LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
D. LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
E. Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Language Models are Few-Shot Learners
Target entity description: "Language Models are Few-Shot Learners" is a landmark research paper that demonstrated large-scale transformer-based language models can perform diverse tasks from just a few examples without task-specific training.

A. Language Models are Unsupervised Multitask Learners
"Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
B. Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
C. LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
D. LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
E. Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
F. None of above. chosen

Statements (59)

Predicate	Object
instanceOf	research paper ⓘ scientific article ⓘ
alsoKnownAs	GPT-3 paper ⓘ linked to: GPT-3
architecture	transformer ⓘ
author	Aditya Ramesh ⓘ Alec Radford ⓘ Amanda Askell ⓘ Ariel Herbert-Voss ⓘ Arvind Neelakantan ⓘ Benjamin Chess ⓘ Benjamin Mann ⓘ Christopher Berner ⓘ Christopher Hesse ⓘ Clemens Winter ⓘ Daniel M. Ziegler ⓘ Dario Amodei ⓘ Eric Sigler ⓘ Girish Sastry ⓘ Gretchen Krueger ⓘ Ilya Sutskever ⓘ Jack Clark ⓘ Jared Kaplan ⓘ Jeffrey Wu ⓘ Mark Chen ⓘ Mateusz Litwin ⓘ Melanie Subbiah ⓘ Nick Ryder ⓘ Prafulla Dhariwal ⓘ Pranav Shyam ⓘ Rewon Child ⓘ Sam McCandlish ⓘ Sandhini Agarwal ⓘ Scott Gray ⓘ Tom B. Brown ⓘ Tom Henighan ⓘ
demonstrates	few-shot learning capabilities of large language models ⓘ one-shot learning capabilities of large language models ⓘ zero-shot learning capabilities of large language models ⓘ
field	artificial intelligence ⓘ machine learning ⓘ natural language processing ⓘ
impact	landmark paper in large-scale language modeling ⓘ
institution	OpenAI ⓘ
language	English ⓘ
mainSubject	few-shot learning ⓘ large language models ⓘ transformer models ⓘ
modelParameterCount	175 billion ⓘ
proposes	GPT-3 ⓘ
publicationYear	2020 ⓘ
publishedIn	Proceedings of the 34th Conference on Neural Information Processing Systems ⓘ linked to: NeurIPS
publisher	NeurIPS 2020 ⓘ linked to: NeurIPS
shows	performance scaling with model size across many NLP tasks ⓘ
taskTypesEvaluated	cloze tasks ⓘ commonsense reasoning ⓘ question answering ⓘ reading comprehension ⓘ translation ⓘ
title	Language Models are Few-Shot Learners ⓘ

How these facts were elicited

Referenced by (12)

Full triples — surface form annotated when it differs from this entity's canonical label.

Tom B. Brown → notableWork → Language Models are Few-Shot Learners ⓘ

subject linked to: Tom B. Brown et al.

GPT-3 → describedIn → Language Models are Few-Shot Learners ⓘ

subject linked to: Tom B. Brown et al.

Language Models are Few-Shot Learners → title → Language Models are Few-Shot Learners ⓘ

Mark Chen → notableWork → GPT-3: Language Models are Few-Shot Learners ⓘ