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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1335113400 of 17610 papers

TitleStatusHype
Language Model Metrics and Procrustes Analysis for Improved Vector Transformation of NLP Embeddings0
Template-Based Named Entity Recognition Using BARTCode1
nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?0
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation UnderstandingCode1
Provably Secure Generative Linguistic SteganographyCode1
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and AttributionCode1
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
The Limitations of Limited Context for Constituency Parsing0
Luna: Linear Unified Nested AttentionCode1
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics EducationCode1
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling InsightsCode1
Differential Privacy for Text Analytics via Natural Text SanitizationCode1
belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification0
A Span Extraction Approach for Information Extraction on Visually-Rich Documents0
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
A Generalizable Approach to Learning OptimizersCode1
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial AttacksCode0
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers0
Lower Perplexity is Not Always Human-LikeCode0
Learning to Select: A Fully Attentive Approach for Novel Object Captioning0
Transferring Representations of Logical Connectives0
ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI0
Investigating variation in written forms of Nahuatl using character-based language modelsCode0
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining0
Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals0
Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data PredictionCode0
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language ModelsCode0
Are we there yet? Exploring clinical domain knowledge of BERT models0
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection0
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling0
Structural Realization with GGNNs0
Learning and Evaluating a Differentially Private Pre-trained Language Model0
On Randomized Classification Layers and Their Implications in Natural Language Generation0
DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT0
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks0
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers0
Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning0
System description for ProfNER - SMMH: Optimized finetuning of a pretrained transformer and word vectors0
Identification de profil clinique du patient: Une approche de classification de séquences utilisant des modèles de langage français contextualisés (Identification of patient clinical profiles : A sequence classification approach using contextualised French language models )0
LongSumm 2021: Session based automatic summarization model for scientific document0
Nutri-bullets Hybrid: Consensual Multi-document Summarization0
SCRIPT: Self-Critic PreTraining of Transformers0
Ad Headline Generation using Self-Critical Masked Language Model0
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Explainable Multi-hop Verbal Reasoning Through Internal Monologue0
Emotion-Infused Models for Explainable Psychological Stress DetectionCode0
FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models0
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words ExtractionCode0
Target-Aware Data Augmentation for Stance Detection0
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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