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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13651–13700 of 17610 papers

TitleStatusHype
Controllable Generation from Pre-trained Language Models via Inverse PromptingCode1
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation—0
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine TranslationCode1
GPT Understands, TooCode2
GLM: General Language Model Pretraining with Autoregressive Blank InfillingCode3
Structure Inducing Pre-TrainingCode1
Refining Language Models with Compositional ExplanationsCode1
Set-to-Sequence Methods in Machine Learning: a Review—0
Towards Few-Shot Fact-Checking via Perplexity—0
Value-aware Approximate AttentionCode0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words ModelCode0
Advancing RNN Transducer Technology for Speech Recognition—0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
Claim Verification using a Multi-GAN based Model—0
Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation—0
Optimal Embedding Calibration for Symbolic Music Similarity—0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting—0
Inductive Relation Prediction by BERTCode1
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation—0
Evaluation of Morphological Embeddings for English and Russian Languages—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
On Improving Deep Learning Trace Analysis with System Call Arguments—0
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text RecognitionCode1
MERMAID: Metaphor Generation with Symbolism and Discriminative DecodingCode1
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language ModelsCode1
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification—0
MTLHealth: A Deep Learning System for Detecting Disturbing Content in Student Essays—0
Extracting Semantic Process Information from the Natural Language in Event Logs—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge ServicesCode1
Random Feature Attention—0
University of Copenhagen Participation in TREC Health Misinformation Track 2020—0
Unsupervised Word Segmentation with Bi-directional Neural Language ModelCode0
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics—0
Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines—0
Long Document Summarization in a Low Resource Setting using Pretrained Language Models—0
OmniNet: Omnidirectional Representations from TransformersCode0
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPCode1
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
Chess as a Testbed for Language Model State TrackingCode1
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract MeaningCode1
RoBERTa-wwm-ext Fine-Tuning for Chinese Text ClassificationCode1
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen DomainsCode1
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and VotingCode0
When Attention Meets Fast Recurrence: Training Language Models with Reduced ComputeCode2
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection—0
Evolutionary optimization of contexts for phonetic correction in speech recognition systems—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