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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14701–14750 of 17610 papers

TitleStatusHype
Segatron: Segment-Aware Transformer for Language Modeling and UnderstandingCode1
Investigating Transferability in Pretrained Language ModelsCode0
Revisiting Pre-Trained Models for Chinese Natural Language ProcessingCode3
Modeling Long Context for Task-Oriented Dialogue State Generation—0
Meta-Transfer Learning for Code-Switched Speech RecognitionCode1
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning—0
Knowledgeable Dialogue Reading Comprehension on Key Turns—0
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning—0
Analysing Lexical Semantic Change with Contextualised Word RepresentationsCode1
Evaluating Transformer-Based Multilingual Text Classification—0
GePpeTto Carves Italian into a Language ModelCode1
Expansion via Prediction of Importance with ContextualizationCode0
Empower Entity Set Expansion via Language Model ProbingCode1
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment AnalysisCode1
Fast and Memory-Efficient Neural Code Completion—0
Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language DetectionCode1
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning—0
A Batch Normalized Inference Network Keeps the KL Vanishing AwayCode1
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System—0
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document MatchingCode0
Assessing Discourse Relations in Language Generation from GPT-2—0
Jointly Trained Transformers models for Spoken Language Translation—0
How fine can fine-tuning be? Learning efficient language models—0
A Tailored Pre-Training Model for Task-Oriented Dialog GenerationCode0
Cross-lingual Information Retrieval with BERTCode0
Contextualized Representations Using Textual Encyclopedic Knowledge—0
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question AnsweringCode1
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word OrderCode1
Lite Transformer with Long-Short Range AttentionCode1
On Adversarial Examples for Biomedical NLP Tasks—0
QURIOUS: Question Generation Pretraining for Text Generation—0
Coupled intrinsic and extrinsic human language resource-based query expansion—0
A Tool for Facilitating OCR Postediting in Historical DocumentsCode0
UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection—0
Residual Energy-Based Models for Text GenerationCode1
Keyphrase Prediction With Pre-trained Language Model—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition—0
Considering Likelihood in NLP Classification Explanations with Occlusion and Language ModelingCode0
Discrete Variational Attention Models for Language Generation—0
Train No Evil: Selective Masking for Task-Guided Pre-TrainingCode1
Music Generation with Temporal Structure Augmentation—0
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network—0
On the Encoder-Decoder Incompatibility in Variational Text Modeling and BeyondCode0
MPNet: Masked and Permuted Pre-training for Language UnderstandingCode2
Adaptive Attention Span in Computer VisionCode1
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised LearningCode1
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
SongNet: Rigid Formats Controlled Text GenerationCode1
Transform and Tell: Entity-Aware News Image CaptioningCode1
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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