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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14251–14300 of 17610 papers

TitleStatusHype
Solving Historical Dictionary Codes with a Neural Language Model—0
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding—0
Online Back-Parsing for AMR-to-Text GenerationCode0
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels—0
Q-learning with Language Model for Edit-based Unsupervised SummarizationCode1
Plug-and-Play Conversational ModelsCode1
Large Product Key Memory for Pretrained Language ModelsCode0
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training—0
Masked ELMo: An evolution of ELMo towards fully contextual RNN language models—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
On the importance of pre-training data volume for compact language models—0
Precise Task Formalization Matters in Winograd Schema EvaluationsCode0
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks—0
Cross-Thought for Sentence Encoder Pre-trainingCode1
"I'd rather just go to bed": Understanding Indirect Answers—0
Inductive Entity Representations from Text via Link PredictionCode1
Beyond [CLS] through Ranking by Generation—0
Converting the Point of View of Messages Spoken to Virtual AssistantsCode0
Compositional Demographic Word EmbeddingsCode1
Guiding Attention for Self-Supervised Learning with TransformersCode1
Learning to Represent Image and Text with Denotation Graph—0
Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model AdaptationCode1
Keep CALM and Explore: Language Models for Action Generation in Text-based GamesCode1
Pretrained Language Model Embryology: The Birth of ALBERTCode1
Rank and run-time aware compression of NLP Applications—0
SPLAT: Speech-Language Joint Pre-Training for Spoken Language UnderstandingCode1
Acrostic Poem Generation—0
Inference Strategies for Machine Translation with Conditional Masking—0
A Pilot Study of Text-to-SQL Semantic Parsing for VietnameseCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Lifelong Language Knowledge DistillationCode1
Linguistic Profiling of a Neural Language Model—0
NLP Service APIs and Models for Efficient Registration of New Clients—0
On Losses for Modern Language ModelsCode1
Static and Animated 3D Scene Generation from Free-form Text DescriptionsCode1
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, UnsupervisedCode0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionCode1
Syntax Representation in Word Embeddings and Neural Networks -- A Survey—0
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding—0
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval—0
XDA: Accurate, Robust Disassembly with Transfer LearningCode1
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence—0
Low-Resource Text Classification via Cross-lingual Language Model Fine-tuning—0
Multi-Reward based Reinforcement Learning for Neural Machine Translation—0
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches—0
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction—0
A Novel Joint Framework for Multiple Chinese Events Extraction—0
Unsupervised Melody Segmentation Based on a Nested Pitman-Yor Language Model—0
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-trainingCode0
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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