XLNet
E435866
autoregressive model
deep learning model
language model
natural language processing model
permutation language model
pretrained model
transformer model
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.
All labels observed (3)
| Label | Occurrences |
|---|---|
| XLNet canonical | 2 |
| XLNet: Generalized Autoregressive Pretraining for Language Understanding | 2 |
| XLNetLMHeadModel | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4389191 — 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: XLNet Context triple: [Hugging Face Transformers, supportsModelType, XLNet]
-
A.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
B.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
C.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
D.
AllenNLP
AllenNLP is an open-source natural language processing research library built on PyTorch, designed to facilitate the development and evaluation of state-of-the-art NLP models.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- 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: XLNet Target entity description: 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.
-
A.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
B.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
C.
GPT-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
D.
AllenNLP
AllenNLP is an open-source natural language processing research library built on PyTorch, designed to facilitate the development and evaluation of state-of-the-art NLP models.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
autoregressive model
ⓘ
deep learning model ⓘ language model ⓘ natural language processing model ⓘ permutation language model ⓘ pretrained model ⓘ transformer model ⓘ |
| achievedStateOfTheArtOn |
GLUE benchmark
ⓘ
RACE dataset ⓘ SQuAD 2.0 ⓘ |
| appliedTo |
natural language inference
ⓘ
question answering ⓘ reading comprehension ⓘ sentiment analysis ⓘ text classification ⓘ |
| availableAs | open-source implementation ⓘ |
| basedOn | Transformer architecture ⓘ |
| comparedTo | BERT ⓘ |
| developedBy |
Carnegie Mellon University
ⓘ
linked to:
CMU
Google Brain ⓘ |
| extends | Transformer-XL ⓘ |
| hasAuthor |
Jaime Carbonell
ⓘ
Quoc V. Le ⓘ Ruslan Salakhutdinov ⓘ Yiming Yang ⓘ Zhilin Yang ⓘ Zihang Dai ⓘ |
| hasFirstAuthor | Zhilin Yang ⓘ |
| hasFullName |
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ⓘ
linked to:
XLNet
|
| hasInput | tokenized text ⓘ |
| hasLicense | Apache License 2.0 ⓘ |
| hasOutput | contextual token representations ⓘ |
| hasProperty |
avoids independence assumption between masked positions
ⓘ
captures bidirectional context ⓘ supports relative positional encoding ⓘ supports segment-level recurrence ⓘ uses autoregressive factorization order ⓘ uses permutation of factorization order ⓘ |
| implementedIn |
PyTorch
ⓘ
TensorFlow ⓘ |
| improvesOn | BERT ⓘ |
| proposedInPaper |
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ⓘ
linked to:
XLNet
|
| publicationYear | 2019 ⓘ |
| publishedIn |
NeurIPS 2019
ⓘ
linked to:
NeurIPS
|
| relatedTo | Transformer-XL ⓘ |
| usedFor |
fine-tuning on downstream tasks
ⓘ
transfer learning in NLP ⓘ |
| usesObjective |
generalized autoregressive pretraining
ⓘ
permutation language modeling ⓘ |
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: XLNet Description of subject: 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.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: XLNet
XLNet
→
proposedInPaper
→
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ⓘ
linked to: XLNet
linked to: XLNet