SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14401–14450 of 17610 papers

TitleStatusHype
Adaptable Multi-Domain Language Model for Transformer ASR—0
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit—0
Hybrid Ranking Network for Text-to-SQLCode1
Transformer with Bidirectional Decoder for Speech Recognition—0
KR-BERT: A Small-Scale Korean-Specific Language ModelCode1
Distilling the Knowledge of BERT for Sequence-to-Sequence ASRCode1
FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire—0
Efficient Neural Query Auto Completion—0
Efficient MDI Adaptation for n-gram Language Models—0
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation—0
Learning Visual Representations with Caption Annotations—0
DeLighT: Deep and Light-weight TransformerCode1
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text SpottingCode1
Learning to Generate Grounded Visual Captions without Localization SupervisionCode1
TweepFake: about Detecting Deepfake TweetsCode1
Future Vector Enhanced LSTM Language Model for LVCSR—0
A Study on Effects of Implicit and Explicit Language Model Information for DBLSTM-CTC Based Handwriting Recognition—0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Improving NER's Performance with Massive financial corpusCode1
On Learning Universal Representations Across Languages—0
Language Modelling for Source Code with Transformer-XLCode0
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large DatasetsCode1
Growing Efficient Deep Networks by Structured Continuous Sparsification—0
COVID-19 therapy target discovery with context-aware literature mining—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
What does BERT know about books, movies and music? Probing BERT for Conversational RecommendationCode1
Text-based classification of interviews for mental health -- juxtaposing the state of the artCode0
Composer Style Classification of Piano Sheet Music Images Using Language Model PretrainingCode0
Compressing Deep Neural Networks via Layer Fusion—0
Mirostat: A Neural Text Decoding Algorithm that Directly Controls PerplexityCode2
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling—0
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection—0
Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language ModelCode0
Online Spatio-Temporal Learning in Deep Neural NetworksCode1
IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News ArticlesCode1
FiSSA at SemEval-2020 Task 9: Fine-tuned For FeelingsCode0
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?—0
Plug-and-Play Conversational Models—0
The Lottery Ticket Hypothesis for Pre-trained BERT NetworksCode1
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR—0
newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda ClassificationCode1
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
One-Shot Learning for Language ModellingCode1
Multi-Perspective Semantic Information Retrieval in the Biomedical Domain—0
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized ArchitecturesCode0
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization—0
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-TrainingCode1
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR—0
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo MethodsCode0
Generative Compositional Augmentations for Scene Graph PredictionCode1
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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