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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10801–10850 of 17610 papers

TitleStatusHype
Extensible Prompts for Language Models on Zero-shot Language Style Customization—0
CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text—0
Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis—0
Language Model Pre-training on True Negatives—0
Language models and brains align due to more than next-word prediction and word-level information—0
A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News HeadlinesCode0
BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?—0
Fast Inference from Transformers via Speculative DecodingCode5
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion—0
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords LearningCode0
Protein Language Models and Structure Prediction: Connection and ProgressionCode1
Improving astroBERT using Semantic Textual Similarity—0
Composition based oxidation state prediction of materials using deep learningCode1
Better Transcription of UK Supreme Court Hearings—0
Coder Reviewer Reranking for Code GenerationCode1
Syntactic Substitutability as Unsupervised Dependency SyntaxCode0
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language ModelsCode0
Continuous diffusion for categorical data—0
DiffusionBERT: Improving Generative Masked Language Models with Diffusion ModelsCode2
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Fine-tuning language models to find agreement among humans with diverse preferences—0
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All—0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification—0
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5—0
Multi-Modal Few-Shot Temporal Action DetectionCode1
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
GPT-3-driven pedagogical agents for training children's curious question-asking skills—0
Comparison Study Between Token Classification and Sequence Classification In Text Classification—0
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text LabelsCode2
Bidirectional Representations for Low Resource Spoken Language Understanding—0
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion LearningCode1
Question Answering and Question Generation for Finnish—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Unified Multimodal Model with Unlikelihood Training for Visual DialogCode1
Word-Level Representation From Bytes For Language Modeling—0
Open-vocabulary Attribute DetectionCode1
TorchScale: Transformers at Scale—0
Retrieval-Augmented Multimodal Language Modeling—0
MarianCG: a code generation transformer model inspired by machine translationCode1
Knowledge Prompting for Few-shot Action Recognition—0
Converge to the Truth: Factual Error Correction via Iterative Constrained EditingCode0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
Coreference Resolution through a seq2seq Transition-Based System—0
Visually Grounded Commonsense Knowledge AcquisitionCode1
Validating Large Language Models with ReLMCode1
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model—0
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image ClassificationCode1
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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