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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14951–15000 of 17610 papers

TitleStatusHype
Language Modelling with NMT Query Translation for Amharic-Arabic Cross-Language Information Retrieval—0
Robust Text Classification using Sub-Word Information in Input Word Representations.—0
Mixtape: Breaking the Softmax Bottleneck Efficiently—0
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) TimeCode0
Memory Efficient Adaptive Optimization—0
DATA: Differentiable ArchiTecture ApproximationCode0
Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss GermanCode0
Neural language modeling of free word order argument structure—0
Pythia: AI-assisted Code Completion SystemCode0
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation—0
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition—0
Inducing Relational Knowledge from BERT—0
SimpleBooks: Long-term dependency book dataset with simplified English vocabulary for word-level language modeling—0
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection—0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction—0
Autoencoding Undirected Molecular Graphs With Neural NetworksCode0
Single Headed Attention RNN: Stop Thinking With Your HeadCode0
Relevance-Promoting Language Model for Short-Text Conversation—0
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural InformationCode0
Independent language modeling architecture for end-to-end ASR—0
Emotional Neural Language Generation Grounded in Situational ContextsCode0
Learning to Learn Words from Visual ScenesCode0
Rigging the Lottery: Making All Tickets WinnersCode1
Gating Revisited: Deep Multi-layer RNNs That Can Be TrainedCode0
Unsupervised Domain Adaptation of Language Models for Reading Comprehension—0
Improving EEG based Continuous Speech Recognition—0
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview—0
Controlling the Amount of Verbatim Copying in Abstractive SummarizationCode0
Continual adaptation for efficient machine communicationCode0
Empirical Autopsy of Deep Video Captioning Frameworks—0
Paraphrasing with Large Language Models—0
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation—0
Thick-Net: Parallel Network Structure for Sequential Modeling—0
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern ArchitecturesCode0
Unsupervised Natural Question Answering with a Small Model—0
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies—0
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
Sparse associative memory based on contextual code learning for disambiguating word senses—0
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence ModellingCode0
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
Compressive Transformers for Long-Range Sequence ModellingCode1
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse DataCode1
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
Neural Architecture Search for Natural Language Understanding—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