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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 12501–12550 of 17610 papers

TitleStatusHype
Contextual Representation Learning beyond Masked Language Modeling—0
Bootstrapping Text Anonymization Models with Distant Supervision—0
Generated Knowledge Prompting for Commonsense Reasoning—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
How does the pre-training objective affect what large language models learn about linguistic properties?—0
Enhancing Robustness of Pre-trained Language Model with Lexical Simplification—0
Contrastive Learning for Low Resource Machine Translation—0
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method—0
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
End-to-end Reference-free Single-document Summary Quality Assessment—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Generative Prompt Tuning for Relation Classification—0
Better Language Model with Hypernym Class Prediction—0
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level—0
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog—0
End-to-end Task-oriented Dialog Policy Learning based on Pre-trained Language Model—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Improving Unsupervised Sentence Simplification Using Fine-Tuned Masked Language Models—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
Using Structured Content Plans for Fine-grained Syntactic Control in Pretrained Language Model Generation—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
Phrase-aware Unsupervised Constituency Parsing—0
Schema-Free Dependency Parsing via Sequence Generation—0
Plug-Tagger: A Pluggable Sequence Labeling Framework with Pre-trained Language Models—0
Towards a Progression-Aware Autonomous Dialogue Agent—0
TACO: Pre-training of Deep Transformers with Attention Convolution using Disentangled Positional Representation—0
StableMoE: Stable Routing Strategy for Mixture of Experts—0
Meta-learning via Language Model In-context Tuning—0
Temporal Language Modeling for Short Text Document Classification with Transformers—0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models—0
Towards Unified Prompt Tuning for Few-shot Learning—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Prompt-Learning for Fine-Grained Entity Typing—0
Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-visual Speech Recognition—0
Tokenization on the Number Line is All You Need—0
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification—0
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction—0
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks—0
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction—0
Text-to-Table: A New Way of Information Extraction—0
NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
MIMICause: Representation and automatic extraction of causal relation types from clinical notes—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Invariant Language Modeling—0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Show:102550
← PrevPage 251 of 353Next →
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