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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13201–13250 of 17610 papers

TitleStatusHype
gaBERT -- an Irish Language ModelCode1
Cross-lingual Transferring of Pre-trained Contextualized Language Models—0
Fine-Grained Emotion Prediction by Modeling Emotion DefinitionsCode0
Exploiting Language Model for Efficient Linguistic Steganalysis—0
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for SequencesCode1
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
FNetAR: Mixing Tokens with Autoregressive Fourier TransformsCode0
DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines—0
Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification—0
Neuradicon: operational representation learning of neuroimaging reports—0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding—0
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System—0
Seed Words Based Data Selection for Language Model Adaptation—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate SpeechCode1
A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking—0
Using Language Models on Low-end Hardware—0
TAPEX: Table Pre-training via Learning a Neural SQL ExecutorCode1
Intersectional Bias in Causal Language Models—0
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan—0
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning SkillsCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
DeepMutants: Training neural bug detectors with contextual mutations—0
Deduplicating Training Data Makes Language Models BetterCode2
A Note on Learning Rare Events in Molecular Dynamics using LSTM and TransformerCode0
HTLM: Hyper-Text Pre-Training and Prompting of Language Models—0
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched TextCode0
From Show to Tell: A Survey on Deep Learning-based Image Captioning—0
Large-Scale News Classification using BERT Language Model: Spark NLP Approach—0
ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 editionCode0
Codified audio language modeling learns useful representations for music information retrievalCode1
Combiner: Full Attention Transformer with Sparse Computation CostCode0
BERT-like Pre-training for Symbolic Piano Music Classification TasksCode1
MOOCRep: A Unified Pre-trained Embedding of MOOC EntitiesCode0
Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative WritingCode0
Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity—0
Differentiable Random Access Memory using LatticesCode0
Evaluating Large Language Models Trained on CodeCode3
Controlled Caption Generation for Images Through Adversarial Attacks—0
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
DeepRapper: Neural Rap Generation with Rhyme and Rhythm ModelingCode0
Long-Short Transformer: Efficient Transformers for Language and VisionCode1
Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text KernelCode1
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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