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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14451–14500 of 17610 papers

TitleStatusHype
Neural Composition: Learning to Generate from Multiple Models—0
Multi-Dialect Arabic BERT for Country-Level Dialect IdentificationCode1
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Conditioned Time-Dilated Convolutions for Sound Event Detection—0
Language Modeling with Reduced Densities—0
NLP Service APIs and Models for Efficient Registration of New Clients—0
The Go Transformer: Natural Language Modeling for Game Play—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
Learning Spoken Language Representations with Neural Lattice Language ModelingCode1
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
Language-agnostic BERT Sentence EmbeddingCode1
Processing South Asian Languages Written in the Latin Script: the Dakshina DatasetCode1
The Impact of Explanations on AI Competency Prediction in VQA—0
Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge—0
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
Enhancing Transformer with Sememe Knowledge—0
An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Assisting Undergraduate Students in Writing Spanish Methodology Sections—0
DeepMet: A Reading Comprehension Paradigm for Token-level Metaphor Detection—0
AI Sensing for Robotics using Deep Learning based Visual and Language Modeling—0
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems—0
Can Wikipedia Categories Improve Masked Language Model Pretraining?—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Using Social Media For Bitcoin Day Trading Behavior Prediction—0
Tigrinya Automatic Speech recognition with Morpheme based recognition units—0
Monolingual corpus creation and evaluation of truly low-resource languages from Peru—0
Long-Tail Predictions with Continuous-Output Language Models—0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition—0
To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks—0
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models—0
Semi-supervised Contextual Historical Text Normalization—0
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation—0
Max-Margin Incremental CCG Parsing—0
Modeling Code-Switch Languages Using Bilingual Parallel Corpus—0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions—0
Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model—0
Do Transformers Need Deep Long-Range Memory?—0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning—0
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis—0
A Mixture of h - 1 Heads is Better than h Heads—0
Automatic Poetry Generation from Prosaic Text—0
What Does BERT with Vision Look At?—0
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation—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