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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11401–11450 of 17610 papers

TitleStatusHype
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject StudiesCode1
A Survey on Open Information Extraction from Rule-based Model to Large Language Model—0
Utilizing Language Models to Expand Vision-Based Commonsense Knowledge GraphsCode0
Visual Comparison of Language Model Adaptation—0
Dual Modality Prompt Tuning for Vision-Language Pre-Trained ModelCode1
Ask Question First for Enhancing Lifelong Language LearningCode0
Neural Embeddings for Text—0
LLM.int8(): 8-bit Matrix Multiplication for Transformers at ScaleCode5
Syntax-driven Data Augmentation for Named Entity RecognitionCode0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
LM-CORE: Language Models with Contextually Relevant External KnowledgeCode0
Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation—0
Reducing Retraining by Recycling Parameter-Efficient Prompts—0
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
CoditT5: Pretraining for Source Code and Natural Language EditingCode1
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in RadiologyCode0
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree SearchCode1
DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning—0
Thai Wav2Vec2.0 with CommonVoice V8Code0
GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-trainingCode1
When can I Speak? Predicting initiation points for spoken dialogue agentsCode0
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected LossCode4
Fusing Sentence Embeddings Into LSTM-based Autoregressive Language ModelsCode0
VQ-T: RNN Transducers using Vector-Quantized Prediction Network States—0
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language—0
Masked Vision and Language Modeling for Multi-modal Representation Learning—0
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Composable Text Controls in Latent Space with ODEsCode1
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
On the Limitations of Sociodemographic Adaptation with TransformersCode0
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets—0
Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing—0
Neural Knowledge Bank for Pretrained Transformers—0
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage RetrievalCode1
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics—0
Smoothing Entailment Graphs with Language ModelsCode0
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
Sequence to sequence pretraining for a less-resourced Slovenian languageCode0
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative—0
CrAM: A Compression-Aware MinimizerCode1
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions—0
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation—0
Boosting Point-BERT by Multi-choice TokensCode0
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records—0
Training Effective Neural Sentence Encoders from Automatically Mined ParaphrasesCode1
A Hazard Analysis Framework for Code Synthesis Large Language Models—0
A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries—0
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
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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