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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16101–16150 of 17610 papers

TitleStatusHype
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNCode0
From Nodes to Networks: Evolving Recurrent Neural Networks—0
Entity-Aware Language Model as an Unsupervised Reranker—0
Learning Approximate Inference Networks for Structured PredictionCode1
The Importance of Being Recurrent for Modeling Hierarchical StructureCode0
Deep-FSMN for Large Vocabulary Continuous Speech RecognitionCode0
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
On Modular Training of Neural Acoustics-to-Word Model for LVCSR—0
Syntax-Aware Language Modeling with Recurrent Neural Networks—0
Semi-Supervised Neural Machine Translation with Language Models—0
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning—0
Memory-based Parameter Adaptation—0
Entropy Rate Estimation for Markov Chains with Large State Space—0
Variational Autoencoders for Collaborative FilteringCode0
Variational Autoencoders for Collaborative FilteringCode1
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy—0
Deep contextualized word representationsCode1
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Efficient Neural Architecture Search via Parameter SharingCode1
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence LearningCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep LearningCode0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
Nested LSTMsCode0
Accelerating recurrent neural network language model based online speech recognition system—0
Discrete Autoencoders for Sequence ModelsCode0
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error CorrectionCode0
Universal Language Model Fine-tuning for Text ClassificationCode3
Enhancing Translation Language Models with Word Embedding for Information Retrieval—0
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling—0
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer—0
From Information Bottleneck To Activation Norm Penalty—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
Dense Recurrent Neural Network with Attention Gate—0
A Goal-oriented Neural Conversation Model by Self-Play—0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS—0
Convolutional Sequence Modeling Revisited—0
Gated ConvNets for Letter-Based ASR—0
Training RNNs as Fast as CNNsCode2
Revisiting Bayes by Backprop—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures—0
Learning Document Embeddings With CNNs—0
Learning to Write by Learning the Objective—0
Noise-Based Regularizers for Recurrent Neural Networks—0
Realtime query completion via deep language models—0
Distributed Fine-tuning of Language Models on Private Data—0
Predictive power of word surprisal for reading times is a linear function of language model quality—0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction—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