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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1420114250 of 17610 papers

TitleStatusHype
M2D: A Multi-modal Framework for Automatic Medical Diagnosis0
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients0
Cold-start Active Learning through Self-supervised Language ModelingCode1
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering0
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline0
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation0
Towards Automatic Sentiment-based Topic Phrase Generation0
QuesBELM: A BERT based Ensemble Language Model for Natural Questions0
Hierarchical Multitask Learning Approach for BERT0
Consistency and Coherency Enhanced Story Generation0
Knowledge-Grounded Dialogue Generation with Pre-trained Language ModelsCode1
Question Answering over Knowledge Base using Language Model Embeddings0
Human Adversarial QA: Did the Model Understand the Paragraph?0
Detecting ESG topics using domain-specific language models and data augmentation approaches0
Substance over Style: Document-Level Targeted Content TransferCode0
Multi-task Learning of Negation and Speculation for Targeted Sentiment ClassificationCode0
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets -- RoBERTa Ensembles and The Continued Relevance of Handcrafted FeaturesCode0
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses0
A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation0
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training ApproachCode1
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed GradientsCode2
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense GraphsCode1
Pretrained Language Models for Dialogue Generation with Multiple Input SourcesCode1
Memformer: A Memory-Augmented Transformer for Sequence Modeling0
Text Classification Using Label Names Only: A Language Model Self-Training ApproachCode1
Chinese Lexical SimplificationCode1
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?0
Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded SupervisionCode1
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion0
Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource LanguageCode1
Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring0
Are Some Words Worth More than Others?Code0
BioMegatron: Larger Biomedical Domain Language Model0
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model0
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures0
Human-centric Dialog Training via Offline Reinforcement Learning0
Meta-Context Transformers for Domain-Specific Response GenerationCode0
Pre-trained Language Model Based Active Learning for Sentence Matching0
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language ModelingCode0
Multi-Stage Pre-training for Low-Resource Domain Adaptation0
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task0
Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task0
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLUCode0
Beyond Language: Learning Commonsense from Images for ReasoningCode0
What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional EncodingCode1
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information MaximizationCode0
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?Code0
Toward Micro-Dialect Identification in Diaglossic and Code-Switched EnvironmentsCode1
Discourse structure interacts with reference but not syntax in neural language modelsCode0
ChrEn: Cherokee-English Machine Translation for Endangered Language RevitalizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified