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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16201–16250 of 17610 papers

TitleStatusHype
Text Generation Based on Generative Adversarial Nets with Latent VariableCode0
Preventing Gradient Explosions in Gated Recurrent Units—0
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading—0
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks—0
Language Modeling with Recurrent Highway Hypernetworks—0
The power of absolute discounting: all-dimensional distribution estimation—0
Visual Features for Context-Aware Speech Recognition—0
Deep Learning Scaling is Predictable, Empirically—0
Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks—0
Chinese Spelling Check based on N-gram and String Matching Algorithm—0
Slim Embedding Layers for Recurrent Neural Language Models—0
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
Z-Forcing: Training Stochastic Recurrent NetworksCode0
ACtuAL: Actor-Critic Under Adversarial Learning—0
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelCode0
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Large-scale Cloze Test Dataset Created by Teachers—0
Language Modeling for Code-Switched Data: Challenges and Approaches—0
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations—0
Block-Sparse Recurrent Neural Networks—0
Cortical microcircuits as gated-recurrent neural networks—0
Unbounded cache model for online language modeling with open vocabularyCode0
Multilingual Speech Recognition With A Single End-To-End Model—0
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models—0
Dual Language Models for Code Switched Speech Recognition—0
Neural Language Modeling by Jointly Learning Syntax and LexiconCode0
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Kyoto University Participation to WAT 2017Code0
On Modeling Sense Relatedness in Multi-prototype Word Embedding—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel CorpusCode0
Hyperspherical Query Likelihood Models with Word Embeddings—0
Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—0
Improving Black-box Speech Recognition using Semantic Parsing—0
Can Discourse Relations be Identified Incrementally?—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
Fraternal DropoutCode0
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue ModelingCode0
Tensor network language model—0
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models—0
Rotational Unit of MemoryCode0
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGACode0
Low-Rank RNN Adaptation for Context-Aware Language ModelingCode0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Counterfactual Language Model Adaptation for Suggesting PhrasesCode0
Person Re-Identification with Vision and Language—0
Normalizador de Texto para Lingua Portuguesa baseado em Modelo de Linguagem (A Normalizer based on Language Model for Texts in Portuguese)[In Portuguese]—0
Generating Sentences by Editing PrototypesCode0
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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