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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15651–15700 of 17610 papers

TitleStatusHype
Review Conversational Reading ComprehensionCode0
Multi-Task Deep Neural Networks for Natural Language UnderstandingCode0
A Generalized Language Model in Tensor SpaceCode0
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference—0
Memory-Efficient Adaptive OptimizationCode0
Tensorized Embedding Layers for Efficient Model CompressionCode0
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM—0
Latent Normalizing Flows for Discrete SequencesCode0
Pay Less Attention with Lightweight and Dynamic ConvolutionsCode1
Glyce: Glyph-vectors for Chinese Character RepresentationsCode0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Variational Smoothing in Recurrent Neural Network Language Models—0
Language Model Pre-training for Hierarchical Document Representations—0
State-Regularized Recurrent Neural Networks—0
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
Cross-lingual Language Model PretrainingCode0
Error-Correcting Neural Sequence Prediction—0
Robust Chinese Word Segmentation with Contextualized Word Representations—0
Towards Using Context-Dependent Symbols in CTC Without State-Tying Decision Trees—0
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models—0
Transformer-XL: Attentive Language Models Beyond a Fixed-Length ContextCode1
What do Language Representations Really Represent?—0
Team Papelo: Transformer Networks at FEVERCode0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network—0
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder—0
Adaptive User Modeling with Long and Short-Term Preferences for Personalized RecommendationCode0
Neural and rule-based Finnish NLP models---expectations, experiments and experiences—0
Transfer learning from language models to image caption generators: Better models may not transfer betterCode0
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units—0
ATHENA: Automated Tuning of Genomic Error Correction Algorithms using Language Models—0
Exploring Weight Symmetry in Deep Neural NetworksCode0
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation—0
Knowledge Representation Learning: A Quantitative ReviewCode2
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling—0
Hierarchical LSTMs with Adaptive Attention for Visual Captioning—0
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems—0
Precision Highway for Ultra Low-Precision Quantization—0
Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text RecognitionCode1
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching—0
What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP ModelsCode1
Fully Convolutional Speech Recognition—0
Conditional BERT Contextual AugmentationCode0
Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images—0
Deep learning incorporating biologically-inspired neural dynamicsCode0
Learning Private Neural Language Modeling with Attentive AggregationCode0
Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling TaskCode0
Scalable language model adaptation for spoken dialogue systems—0
Layer Flexible Adaptive Computational Time—0
Neural Abstractive Text Summarization with Sequence-to-Sequence ModelsCode1
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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