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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 34513500 of 17610 papers

TitleStatusHype
Sentence Bottleneck Autoencoders from Transformer Language ModelsCode1
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot LearnersCode1
Selective Differential Privacy for Language ModelingCode1
Want To Reduce Labeling Cost? GPT-3 Can HelpCode1
Dealing with Typos for BERT-based Passage Retrieval and RankingCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Semantic-Based Self-Critical Training For Question GenerationCode1
SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionCode1
From Two to One: A New Scene Text Recognizer with Visual Language Modeling NetworkCode1
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text MiningCode1
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You NeedCode1
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User ProfilesCode1
Knowledge Perceived Multi-modal Pretraining in E-commerceCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Modeling Protein Using Large-scale Pretrain Language ModelCode1
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage RetrievalCode1
DEMix Layers: Disentangling Domains for Modular Language ModelingCode1
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
Noisy Channel Language Model Prompting for Few-Shot Text ClassificationCode1
Finetuning Pretrained Transformers into Variational AutoencodersCode1
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
Controlled Text Generation as Continuous Optimization with Multiple ConstraintsCode1
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text ClassificationCode1
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space TranslationCode1
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling CorrectionCode1
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
Controllable Sentence Simplification with a Unified Text-to-Text Transfer TransformerCode1
eMLM: A New Pre-training Objective for Emotion Related TasksCode1
Structural Guidance for Transformer Language ModelsCode1
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingCode1
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem SolvingCode1
gaBERT -- an Irish Language ModelCode1
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for SequencesCode1
Brazilian Portuguese Speech Recognition Using Wav2vec 2.0Code1
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate SpeechCode1
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
TAPEX: Table Pre-training via Learning a Neural SQL ExecutorCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning SkillsCode1
Codified audio language modeling learns useful representations for music information retrievalCode1
BERT-like Pre-training for Symbolic Piano Music Classification TasksCode1
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
Long-Short Transformer: Efficient Transformers for Language and VisionCode1
Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text KernelCode1
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language ModelingCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
XLM-E: Cross-lingual Language Model Pre-training via ELECTRACode1
Stabilizing Equilibrium Models by Jacobian RegularizationCode1
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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