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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12451–12500 of 17610 papers

TitleStatusHype
Training a T5 Using Lab-sized Resources—0
PEER: A Collaborative Language Model—0
Repair Is Nearly Generation: Multilingual Program Repair with LLMs—0
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language ModelsCode0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning—0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers—0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Framework—0
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes—0
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks—0
VLMAE: Vision-Language Masked Autoencoder—0
A Survey on Open Information Extraction from Rule-based Model to Large Language Model—0
Neural Embeddings for Text—0
Ask Question First for Enhancing Lifelong Language LearningCode0
Utilizing Language Models to Expand Vision-Based Commonsense Knowledge GraphsCode0
Visual Comparison of Language Model Adaptation—0
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
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in RadiologyCode0
DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning—0
Thai Wav2Vec2.0 with CommonVoice V8Code0
When can I Speak? Predicting initiation points for spoken dialogue agentsCode0
Fusing Sentence Embeddings Into LSTM-based Autoregressive Language ModelsCode0
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language—0
Masked Vision and Language Modeling for Multi-modal Representation Learning—0
VQ-T: RNN Transducers using Vector-Quantized Prediction Network States—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
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
On the Limitations of Sociodemographic Adaptation with TransformersCode0
Neural Knowledge Bank for Pretrained Transformers—0
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics—0
Smoothing Entailment Graphs with Language ModelsCode0
Sequence to sequence pretraining for a less-resourced Slovenian languageCode0
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions—0
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative—0
Boosting Point-BERT by Multi-choice TokensCode0
SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation—0
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records—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