Attention Is All You Need
E457850
"Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Attention Is All You Need canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4651095 — 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: Attention Is All You Need Context triple: [Transformer, introducedInPaper, Attention Is All You Need]
-
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.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
D.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
-
E.
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.
- 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: Attention Is All You Need Target entity description: "Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
-
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.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
C.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
D.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
-
E.
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.
- F. None of above. chosen
Statements (53)
| Predicate | Object |
|---|---|
| instanceOf |
computer science paper
ⓘ
research paper ⓘ scientific paper ⓘ |
| affiliatedInstitution |
Google Brain
NERFINISHED
ⓘ
Google Research NERFINISHED ⓘ |
| applicationDomain | machine translation ⓘ |
| architectureType | encoder-decoder ⓘ |
| benchmarkDataset |
WMT 2014 English-to-French translation
NERFINISHED
ⓘ
WMT 2014 English-to-German translation NERFINISHED ⓘ |
| citationStatus | highly cited paper ⓘ |
| enabled | parallel training of sequence models ⓘ |
| field |
deep learning
ⓘ
machine learning ⓘ natural language processing ⓘ sequence modeling ⓘ |
| hasAuthor |
Aidan N. Gomez
NERFINISHED
ⓘ
Ashish Vaswani NERFINISHED ⓘ Illia Polosukhin NERFINISHED ⓘ Jakob Uszkoreit NERFINISHED ⓘ Llion Jones NERFINISHED ⓘ Niki Parmar NERFINISHED ⓘ Noam Shazeer NERFINISHED ⓘ Łukasz Kaiser NERFINISHED ⓘ |
| impact |
became foundational for large language models
ⓘ
revolutionized modern natural language processing ⓘ |
| inspiredModel |
BERT
NERFINISHED
ⓘ
GPT series NERFINISHED ⓘ T5 NERFINISHED ⓘ |
| introducedConcept |
Transformer architecture
ⓘ
multi-head attention ⓘ positional encoding ⓘ scaled dot-product attention ⓘ self-attention mechanism ⓘ |
| optimizationMethod | Adam optimizer NERFINISHED ⓘ |
| outperformed | previous state-of-the-art machine translation models ⓘ |
| proposedModel | Transformer NERFINISHED ⓘ |
| publicationYear | 2017 ⓘ |
| publishedIn | Advances in Neural Information Processing Systems 30 NERFINISHED ⓘ |
| publishedInConference | NeurIPS 2017 NERFINISHED ⓘ |
| publisher | Neural Information Processing Systems Foundation NERFINISHED ⓘ |
| reduced | sequential computation in sequence models ⓘ |
| replacedArchitecture |
GRU networks
ⓘ
LSTM networks NERFINISHED ⓘ recurrent neural networks ⓘ |
| title | Attention Is All You Need NERFINISHED ⓘ |
| usesComponent |
dropout regularization
ⓘ
layer normalization ⓘ multi-head self-attention layers ⓘ position-wise feed-forward networks ⓘ residual connections ⓘ stacked decoder layers ⓘ stacked encoder layers ⓘ |
| usesTechnique | label smoothing ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Attention Is All You Need Description of subject: "Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Łukasz Kaiser