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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14151–14200 of 17610 papers

TitleStatusHype
OmniNet: Omnidirectional Representations from TransformersCode0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection—0
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and VotingCode0
Evolutionary optimization of contexts for phonetic correction in speech recognition systems—0
Enhanced Modality Transition for Image Captioning—0
Bilingual Language Modeling, A transfer learning technique for Roman Urdu—0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Web-based Application for Detecting Indonesian Clickbait Headlines using IndoBERT—0
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input—0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics—0
Do End-to-End Speech Recognition Models Care About Context?—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT—0
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations—0
MSA Transformer—0
Transformer-Based Approaches for Automatic Music TranscriptionCode0
Speech-language Pre-training for End-to-end Spoken Language Understanding—0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation—0
Customizing Contextualized Language Models forLegal Document Reviews—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application—0
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers—0
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models—0
Intermediate Loss Regularization for CTC-based Speech Recognition—0
Understanding Emails and Drafting Responses -- An Approach Using GPT-3—0
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models—0
Adaptive Semiparametric Language Models—0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy—0
Mind the Gap: Assessing Temporal Generalization in Neural Language Models—0
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition—0
Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)—0
End2End Acoustic to Semantic Transduction—0
SJ_AJ@DravidianLangTech-EACL2021: Task-Adaptive Pre-Training of Multilingual BERT models for Offensive Language IdentificationCode0
Adversarial Contrastive Pre-training for Protein Sequences—0
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models—0
Speech Recognition by Simply Fine-tuning BERT—0
N-grams Bayesian Differential Privacy—0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge—0
BERTaú: Itaú BERT for digital customer service—0
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting—0
Explaining Natural Language Processing Classifiers with Occlusion and Language ModelingCode0
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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