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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15101–15150 of 17610 papers

TitleStatusHype
Federated Evaluation of On-device Personalization—0
IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and AnalysisCode0
Automatic Extraction of Personality from Text: Challenges and Opportunities—0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Transformer-based Acoustic Modeling for Hybrid Speech Recognition—0
Localization of Fake News Detection via Multitask Transfer LearningCode0
Improving Sequence Modeling Ability of Recurrent Neural Networks via SememesCode0
ELSA: A Throughput-Optimized Design of an LSTM Accelerator for Energy-Constrained Devices—0
ALOHA: Artificial Learning of Human Attributes for Dialogue AgentsCode0
BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge—0
Memory-Augmented Recurrent Networks for Dialogue Coherence—0
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model PerformanceCode0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Rethinking Exposure Bias In Language Modeling—0
Stabilizing Transformers for Reinforcement LearningCode1
Neural Memory Plasticity for Anomaly Detection—0
VAIS ASR: Building a conversational speech recognition system using language model combination—0
Deep Independently Recurrent Neural Network (IndRNN)Code0
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers ModelsCode0
Neural Generation for Czech: Data and BaselinesCode0
Link Prediction via Graph Attention Network—0
Structured Pruning of Large Language ModelsCode1
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-gramsCode0
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models—0
On the adequacy of untuned warmup for adaptive optimizationCode0
Is Multilingual BERT Fluent in Language Generation?Code0
Do People Prefer "Natural" code?—0
Federated Learning of N-gram Language Models—0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
ZeRO: Memory Optimizations Toward Training Trillion Parameter ModelsCode1
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System—0
Towards Understanding of Medical Randomized Controlled Trials by Conclusion GenerationCode0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic CuesCode0
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviewsCode0
Neural Generation for Czech: Data and Baselines—0
BERT for Question Generation—0
Generation of Hip-Hop Lyrics with Hierarchical Modeling and Conditional Templates—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Computational Argumentation Synthesis as a Language Modeling Task—0
What goes into a word: generating image descriptions with top-down spatial knowledge—0
Better Document-Level Machine Translation with Bayes' Rule—0
TMLab: Generative Enhanced Model (GEM) for adversarial attacks—0
Latent-Variable Generative Models for Data-Efficient Text Classification—0
Generalization in Generation: A closer look at Exposure Bias—0
Structural Language Models of CodeCode0
Fake news detection using Deep LearningCode0
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers—0
Test-Time Training with Self-Supervision for Generalization under Distribution ShiftsCode0
Show:102550
← PrevPage 303 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