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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14701–14750 of 17610 papers

TitleStatusHype
Future Vector Enhanced LSTM Language Model for LVCSR—0
A Study on Effects of Implicit and Explicit Language Model Information for DBLSTM-CTC Based Handwriting Recognition—0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
COVID-19 therapy target discovery with context-aware literature mining—0
Growing Efficient Deep Networks by Structured Continuous Sparsification—0
Communication-Efficient Federated Learning via Optimal Client Sampling—0
Composer Style Classification of Piano Sheet Music Images Using Language Model PretrainingCode0
Compressing Deep Neural Networks via Layer Fusion—0
Text-based classification of interviews for mental health -- juxtaposing the state of the artCode0
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling—0
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection—0
Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language ModelCode0
FiSSA at SemEval-2020 Task 9: Fine-tuned For FeelingsCode0
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?—0
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR—0
Plug-and-Play Conversational Models—0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
Multi-Perspective Semantic Information Retrieval in the Biomedical Domain—0
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized ArchitecturesCode0
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization—0
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR—0
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo MethodsCode0
Conditioned Time-Dilated Convolutions for Sound Event Detection—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Neural Composition: Learning to Generate from Multiple Models—0
Language Modeling with Reduced Densities—0
The Go Transformer: Natural Language Modeling for Game Play—0
NLP Service APIs and Models for Efficient Registration of New Clients—0
Do Transformers Need Deep Long-Range MemoryCode0
Deep Contextual Embeddings for Address Classification in E-commerce—0
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model—0
CORD19STS: COVID-19 Semantic Textual Similarity Dataset—0
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble MethodsCode0
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation—0
Text Data Augmentation: Towards better detection of spear-phishing emails—0
The Impact of Explanations on AI Competency Prediction in VQA—0
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge—0
Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model—0
Automatic Poetry Generation from Prosaic Text—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
A Mixture of h - 1 Heads is Better than h Heads—0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions—0
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis—0
Assisting Undergraduate Students in Writing Spanish Methodology Sections—0
An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages—0
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems—0
AI Sensing for Robotics using Deep Learning based Visual and Language Modeling—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Enhancing Transformer with Sememe Knowledge—0
DeepMet: A Reading Comprehension Paradigm for Token-level Metaphor Detection—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