Transformer-XL
E701503
Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Transformer-XL canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7874884 — 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: Transformer-XL Context triple: [Layer Normalization, usedIn, Transformer-XL]
-
A.
XLNet
XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
-
B.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
C.
Attention Is All You Need
"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.
-
D.
XLM-R
XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
-
E.
DeBERTa
DeBERTa is a transformer-based language model developed by Microsoft that improves upon BERT and RoBERTa using disentangled attention and enhanced mask decoder mechanisms for superior natural language understanding.
- 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: Transformer-XL Target entity description: Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
-
A.
XLNet
XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
-
B.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
C.
Attention Is All You Need
"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.
-
D.
XLM-R
XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
-
E.
DeBERTa
DeBERTa is a transformer-based language model developed by Microsoft that improves upon BERT and RoBERTa using disentangled attention and enhanced mask decoder mechanisms for superior natural language understanding.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Transformer variant
ⓘ
language model architecture ⓘ neural network architecture ⓘ |
| addressesLimitationOf | standard Transformer context length ⓘ |
| aimsTo | capture long-range dependencies ⓘ |
| appliedTo |
character-level language modeling
ⓘ
word-level language modeling ⓘ |
| benchmarkedOn |
Enwik8
NERFINISHED
ⓘ
One Billion Word Benchmark NERFINISHED ⓘ WikiText-103 NERFINISHED ⓘ |
| describedInPaper | Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context NERFINISHED ⓘ |
| designedFor | language modeling ⓘ |
| developedAt |
Carnegie Mellon University
NERFINISHED
ⓘ
Google Brain NERFINISHED ⓘ |
| evaluationSpeedupReason | reuse of cached hidden states ⓘ |
| extends | Transformer NERFINISHED ⓘ |
| hasFullName | Transformer eXtra Long NERFINISHED ⓘ |
| hasKeyConcept |
decoupling positional encoding from absolute positions
ⓘ
reusing hidden states from previous segments ⓘ |
| improves |
evaluation efficiency
ⓘ
modeling of long-term dependencies ⓘ training efficiency for long sequences ⓘ |
| improvesMetric | perplexity on language modeling benchmarks ⓘ |
| influenced | later long-context Transformer architectures ⓘ |
| introducedFeature |
relative positional encodings
ⓘ
segment-level recurrence ⓘ |
| memoryMechanismType | segment-level recurrence over hidden states ⓘ |
| outperforms | standard Transformer on long-context language modeling benchmarks ⓘ |
| paperPublishedAt | ACL 2019 NERFINISHED ⓘ |
| positionalEncodingType | relative positional encoding ⓘ |
| proposedBy |
Jaime Carbonell
NERFINISHED
ⓘ
Quoc V. Le NERFINISHED ⓘ Ruslan Salakhutdinov NERFINISHED ⓘ William W. Cohen NERFINISHED ⓘ Yiming Yang NERFINISHED ⓘ Zhilin Yang NERFINISHED ⓘ Zihang Dai NERFINISHED ⓘ |
| proposedIn | 2019 ⓘ |
| reduces | context fragmentation ⓘ |
| supports | longer effective context than vanilla Transformer ⓘ |
| trainingObjective | autoregressive language modeling ⓘ |
| uses |
layer normalization
ⓘ
memory mechanism ⓘ multi-head attention ⓘ position-wise feed-forward networks ⓘ relative positional embeddings ⓘ residual connections ⓘ self-attention ⓘ |
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: Transformer-XL Description of subject: Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.