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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 13801–13850 of 17610 papers

TitleStatusHype
Towards Continual Entity Learning in Language Models for Conversational Agents—0
Goal-Oriented Script ConstructionCode0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
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
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification—0
DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines—0
FNetAR: Mixing Tokens with Autoregressive Fourier TransformsCode0
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
A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking—0
Using Language Models on Low-end Hardware—0
Intersectional Bias in Causal Language Models—0
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Large-Scale News Classification using BERT Language Model: Spark NLP Approach—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
A Note on Learning Rare Events in Molecular Dynamics using LSTM and TransformerCode0
HTLM: Hyper-Text Pre-Training and Prompting of Language Models—0
DeepMutants: Training neural bug detectors with contextual mutations—0
ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 editionCode0
MOOCRep: A Unified Pre-trained Embedding of MOOC EntitiesCode0
Combiner: Full Attention Transformer with Sparse Computation CostCode0
Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative WritingCode0
Controlled Caption Generation for Images Through Adversarial Attacks—0
Differentiable Random Access Memory using LatticesCode0
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity—0
Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS—0
LanguageRefer: Spatial-Language Model for 3D Visual Grounding—0
DeepRapper: Neural Rap Generation with Rhyme and Rhythm ModelingCode0
Getting to Production with Few-shot Natural Language Generation Models—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Projection of Turn Completion in Incremental Spoken Dialogue Systems—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models—0
What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval—0
Visual Conceptual Blending with Large-scale Language and Vision Models—0
Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models—0
Language Models are Good Translators—0
Multimodal Few-Shot Learning with Frozen Language Models—0
Learning to Sample Replacements for ELECTRA Pre-Training—0
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus—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