Exploring the Limits of Language Modeling
E260053
"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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Exploring the Limits of Language Modeling canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2373687 — 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: Exploring the Limits of Language Modeling Context triple: [Quoc V. Le, coAuthorOf, Exploring the Limits of Language Modeling]
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
Mathematical Structures of Language
Mathematical Structures of Language is a foundational work in mathematical linguistics that applies formal and algebraic methods to analyze the structure of natural languages.
-
C.
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.
-
D.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
E.
Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
- 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: Exploring the Limits of Language Modeling Target entity description: "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.
-
A.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
-
B.
Mathematical Structures of Language
Mathematical Structures of Language is a foundational work in mathematical linguistics that applies formal and algebraic methods to analyze the structure of natural languages.
-
C.
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.
-
D.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
E.
Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
- F. None of above. chosen
Statements (36)
| Predicate | Object |
|---|---|
| instanceOf |
research paper
ⓘ
scientific article ⓘ |
| aimsTo |
characterize the limits of language modeling
ⓘ
understand performance scaling laws in language models ⓘ |
| concerns |
evaluation of language model performance
ⓘ
natural language processing benchmarks ⓘ |
| contributesTo |
research on scaling neural networks
ⓘ
theory and practice of language modeling ⓘ understanding of large language models ⓘ |
| examines |
behavior of language models on diverse tasks
ⓘ
impact of scale on language understanding ⓘ trade-offs between model size and performance ⓘ |
| field |
artificial intelligence
ⓘ
machine learning ⓘ natural language processing ⓘ |
| focusesOn |
generalization of language models
ⓘ
natural language tasks ⓘ performance of language models ⓘ scalability of language models ⓘ |
| hasDiscipline |
computational linguistics
ⓘ
computer science ⓘ |
| hasLanguage | English ⓘ |
| hasMedium | academic publication ⓘ |
| investigates |
how far large-scale neural language models can be pushed
ⓘ
limits of language model generalization ⓘ relationship between model scale and performance ⓘ scaling behavior on natural language benchmarks ⓘ |
| mainTopic |
language modeling
ⓘ
large-scale language models ⓘ neural language models ⓘ |
| studies |
large-scale neural language models
ⓘ
statistical properties of language modeling ⓘ |
| typeOfWork |
empirical study
ⓘ
experimental research ⓘ |
| usesMethod |
large-scale training
ⓘ
neural networks ⓘ |
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: Exploring the Limits of Language Modeling Description of subject: "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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.