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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14101–14150 of 17610 papers

TitleStatusHype
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking—0
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models—0
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction—0
Analysing Word Representation from the Input and Output Embeddings in Neural Network Language ModelsCode0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
FENAS: Flexible and Expressive Neural Architecture Search—0
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling—0
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering—0
LIMIT-BERT : Linguistics Informed Multi-Task BERTCode0
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing—0
The Amazing World of Neural Language Generation—0
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER—0
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection—0
Revisiting Representation Degeneration Problem in Language Modeling—0
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization—0
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation—0
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization—0
Personal Information Leakage Detection in ConversationsCode0
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages—0
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments—0
``I'd rather just go to bed'': Understanding Indirect Answers—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
Explainable Clinical Decision Support from Text—0
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling—0
Coding Textual Inputs Boosts the Accuracy of Neural NetworksCode0
ControlVAE: Tuning, Analytical Properties, and Performance AnalysisCode4
Understanding Pre-trained BERT for Aspect-based Sentiment AnalysisCode1
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation—0
SLM: Learning a Discourse Language Representation with Sentence Unshuffling—0
Semantic Labeling Using a Deep Contextualized Language ModelCode0
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach—0
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition—0
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks—0
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model—0
Contextual BERT: Conditioning the Language Model Using a Global State—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
Fusion Models for Improved Visual Captioning—0
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition—0
Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection—0
Multitask Training with Text Data for End-to-End Speech Recognition—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model PretrainingCode1
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer—0
Accelerating Training of Transformer-Based Language Models with Progressive Layer DroppingCode0
Dutch Humor Detection by Generating Negative Examples—0
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text ClassificationCode2
CLPLM: Character Level Pretrained Language Model for Extracting Support Phrases for Sentiment Labels—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