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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 15001–15050 of 17610 papers

TitleStatusHype
RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems—0
Long-span language modeling for speech recognition—0
Conditionally Learn to Pay Attention for Sequential Visual TaskCode0
BP-Transformer: Modelling Long-Range Context via Binary PartitioningCode0
Improving Transformer Models by Reordering their SublayersCode0
Effectiveness of self-supervised pre-training for speech recognitionCode1
Distilling Knowledge Learned in BERT for Text GenerationCode0
CamemBERT: a Tasty French Language ModelCode0
Language Model-Driven Unsupervised Neural Machine Translation—0
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs—0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive BaselinesCode0
On Architectures for Including Visual Information in Neural Language Models for Image DescriptionCode0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
Code-Mixed to Monolingual Translation Framework—0
How Decoding Strategies Affect the Verifiability of Generated TextCode0
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation—0
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck LanguagesCode0
Not Enough Data? Deep Learning to the Rescue!—0
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot FlyCode0
The LIG system for the English-Czech Text Translation Task of IWSLT 2019—0
S2ORC: The Semantic Scholar Open Research CorpusCode0
Improving Grammatical Error Correction with Machine Translation PairsCode0
Blockwise Self-Attention for Long Document UnderstandingCode0
Open Domain Web Keyphrase Extraction Beyond Language ModelingCode1
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic KnowledgeCode0
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds—0
A Programmable Approach to Neural Network CompressionCode0
Fast Transformer Decoding: One Write-Head is All You NeedCode4
Contextual Grounding of Natural Language Entities in ImagesCode0
RNN-T For Latency Controlled ASR With Improved Beam Search—0
Unsupervised Cross-lingual Representation Learning at ScaleCode1
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
Automatic Detection of Generated Text is Easiest when Humans are FooledCode1
A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models—0
Deep Bidirectional Transformers for Relation Extraction without Supervision—0
Comparing Top-Down and Bottom-Up Neural Generative Dependency Models—0
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding—0
Generalization through Memorization: Nearest Neighbor Language ModelsCode1
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification—0
Experimenting with Power Divergences for Language Modeling—0
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation—0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech—0
Improved Differentiable Architecture Search for Language Modeling and Named Entity RecognitionCode0
Automatic Argument Quality Assessment - New Datasets and Methods—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
MulCode: A Multiplicative Multi-way Model for Compressing Neural Language Model—0
KnowSemLM: A Knowledge Infused Semantic Language Model—0
Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary—0
Show:102550
← PrevPage 301 of 353Next →
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