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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15101–15150 of 17610 papers

TitleStatusHype
Multi-Zone Unit for Recurrent Neural Networks—0
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue—0
A Subword Level Language Model for Bangla Language—0
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence ModellingCode0
Sparse associative memory based on contextual code learning for disambiguating word senses—0
Training a code-switching language model with monolingual data—0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Structured Sparsification of Gated Recurrent Neural Networks—0
Adapting and evaluating a deep learning language model for clinical why-question answering—0
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
Neural Architecture Search for Natural Language Understanding—0
Long-span language modeling for speech recognition—0
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems—0
Conditionally Learn to Pay Attention for Sequential Visual TaskCode0
BP-Transformer: Modelling Long-Range Context via Binary PartitioningCode0
Distilling Knowledge Learned in BERT for Text GenerationCode0
Improving Transformer Models by Reordering their SublayersCode0
CamemBERT: a Tasty French Language ModelCode0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive BaselinesCode0
Language Model-Driven Unsupervised Neural Machine Translation—0
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs—0
On Architectures for Including Visual Information in Neural Language Models for Image DescriptionCode0
How Decoding Strategies Affect the Verifiability of Generated TextCode0
Code-Mixed to Monolingual Translation Framework—0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot FlyCode0
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck LanguagesCode0
Not Enough Data? Deep Learning to the Rescue!—0
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation—0
The LIG system for the English-Czech Text Translation Task of IWSLT 2019—0
Blockwise Self-Attention for Long Document UnderstandingCode0
Improving Grammatical Error Correction with Machine Translation PairsCode0
S2ORC: The Semantic Scholar Open Research CorpusCode0
A Programmable Approach to Neural Network CompressionCode0
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds—0
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic KnowledgeCode0
RNN-T For Latency Controlled ASR With Improved Beam Search—0
Contextual Grounding of Natural Language Entities in ImagesCode0
BAS: An Answer Selection Method Using BERT Language Model—0
Emerging Cross-lingual Structure in Pretrained Language Models—0
BERT-CNN: a Hierarchical Patent Classifier Based on a Pre-Trained Language Model—0
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification—0
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification—0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning—0
A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models—0
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder ParadigmCode0
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation—0
Comparing Top-Down and Bottom-Up Neural Generative Dependency Models—0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech—0
Experimenting with Power Divergences for Language Modeling—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