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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 16501–16550 of 17610 papers

TitleStatusHype
IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT—0
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks—0
Chinese Grammatical Error Diagnosis Using Single Word Embedding—0
Dependency grammars as Haskell programs—0
DSL Shared Task 2016: Perfect Is The Enemy of Good Language Discrimination Through Expectation--Maximization and Chunk-based Language Model—0
How Many Languages Can a Language Model Model?—0
Supervised classification of end-of-lines in clinical text with no manual annotation—0
Japanese Lexical Simplification for Non-Native Speakers—0
Language and Dialect Discrimination Using Compression-Inspired Language Models—0
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation—0
Recurrent Neural Network with Word Embedding for Complaint Classification—0
Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection—0
UQAM-NTL: Named entity recognition in Twitter messages—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
Integrating Optical Character Recognition and Machine Translation of Historical Documents—0
Syntactic and Lexical Complexity in Italian Noncanonical Structures—0
Testing the Processing Hypothesis of word order variation using a probabilistic language model—0
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition—0
Attention-based Memory Selection Recurrent Network for Language Modeling—0
Learning Python Code Suggestion with a Sparse Pointer NetworkCode0
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answeringCode0
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling—0
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Coherent Dialogue with Attention-based Language Models—0
Visualizing Linguistic Shift—0
Variable Computation in Recurrent Neural Networks—0
What Do Recurrent Neural Network Grammars Learn About Syntax?Code0
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media TextCode0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
Gradients of Counterfactuals—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
Quasi-Recurrent Neural NetworksCode0
Neural Architecture Search with Reinforcement LearningCode0
TopicRNN: A Recurrent Neural Network with Long-Range Semantic DependencyCode0
Assessing the Ability of LSTMs to Learn Syntax-Sensitive DependenciesCode0
Tying Word Vectors and Word Classifiers: A Loss Framework for Language ModelingCode1
Using Language Groundings for Context-Sensitive Text Prediction—0
Visualizing the Content of a Children's Story in a Virtual World: Lessons Learned—0
NASTEA: Investigating Narrative Schemas through Annotated Entities—0
Low-resource OCR error detection and correction in French Clinical Texts—0
A Neural Model for Language Identification in Code-Switched Tweets—0
Analyzing Linguistic Knowledge in Sequential Model of Sentence—0
Character Sequence Models for Colorful Words—0
Globally Coherent Text Generation with Neural Checklist Models—0
Event participant modelling with neural networks—0
Convolutional Neural Network Language ModelsCode0
Why Neural Translations are the Right Length—0
Regularizing Text Categorization with Clusters of Words—0
Natural Language Model Re-usability for Scaling to Different Domains—0
Neural Headline Generation on Abstract Meaning Representation—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified