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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1400114050 of 17610 papers

TitleStatusHype
Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers0
Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases0
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model0
Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification0
Arabisc: Context-Sensitive Neural Spelling CheckerCode1
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language ModelingCode1
Neural language models for text classification in evidence-based medicine0
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models0
Communication-Efficient Federated Distillation0
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
Self-Supervised Relationship Probing0
Training Linear Finite-State Machines0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion0
Coarse-to-Fine Memory Matching for Joint Retrieval and ClassificationCode0
Disentangling Homophemes in Lip Reading using Perplexity Analysis0
Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-190
An Investigation of Language Model Interpretability via Sentence EditingCode0
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and HealthCode0
Joint Extraction of Entity and Relation with Information Redundancy Elimination0
Unigram-Normalized Perplexity as a Language Model Performance Measure with Different Vocabulary Sizes0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space0
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo ApproachCode0
Adam^+: A Stochastic Method with Adaptive Variance Reduction0
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models0
Multi-task Language Modeling for Improving Speech Recognition of Rare Words0
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modelingCode1
Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language ModelCode1
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural NetworksCode0
Collaborative Storytelling with Large-scale Neural Language Models0
Self-Supervised learning with cross-modal transformers for emotion recognition0
Predictions For Pre-training Language Models0
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus0
Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification0
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game0
MVP-BERT: Redesigning Vocabularies for Chinese BERT and Multi-Vocab Pretraining0
Neural Semi-supervised Learning for Text Classification Under Large-Scale PretrainingCode1
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter0
Learning Associative Inference Using Fast Weight MemoryCode1
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge BasesCode0
Conditioned Natural Language Generation using only Unconditioned Language Model: An Exploration0
Utilizing Bidirectional Encoder Representations from Transformers for Answer SelectionCode1
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task LearningCode0
Context-aware Stand-alone Neural Spelling CorrectionCode1
Exploring the Value of Personalized Word Embeddings0
E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks0
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration0
Scaling Hidden Markov Language ModelsCode1
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERTCode1
Positional Artefacts Propagate Through Masked Language Model Embeddings0
Adapting a Language Model for Controlled Affective Text GenerationCode1
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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