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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 34013450 of 17610 papers

TitleStatusHype
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillationsCode1
Extracting and Inferring Personal Attributes from DialogueCode1
XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual KnowledgeCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
Zero-Shot Information Extraction as a Unified Text-to-Triple TranslationCode1
Pix2seq: A Language Modeling Framework for Object DetectionCode1
Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language ProcessingCode1
TrOCR: Transformer-based Optical Character Recognition with Pre-trained ModelsCode1
JobBERT: Understanding Job Titles through SkillsCode1
Distilling Linguistic Context for Language Model CompressionCode1
Generative Pre-Training from MoleculesCode1
Context-NER : Contextual Phrase Generation at ScaleCode1
KnowMAN: Weakly Supervised Multinomial Adversarial NetworksCode1
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-trainingCode1
Dialogue State Tracking with a Language Model using Schema-Driven PromptingCode1
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence RepresentationsCode1
Types of Out-of-Distribution Texts and How to Detect ThemCode1
LM-Critic: Language Models for Unsupervised Grammatical Error CorrectionCode1
Rationales for Sequential PredictionsCode1
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuningCode1
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained ModelsCode1
xGQA: Cross-Lingual Visual Question AnsweringCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
TEASEL: A Transformer-Based Speech-Prefixed Language ModelCode1
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary InitializationCode1
Euphemistic Phrase Detection by Masked Language ModelCode1
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-TrainingCode1
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal TransformersCode1
Avoiding Inference Heuristics in Few-shot Prompt-based FinetuningCode1
Efficient Nearest Neighbor Language ModelsCode1
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language ModelsCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling ApproachCode1
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine TranslationCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story BooksCode1
Text-to-Table: A New Way of Information ExtractionCode1
PermuteFormer: Efficient Relative Position Encoding for Long SequencesCode1
Data Efficient Masked Language Modeling for Vision and LanguageCode1
Learning Hierarchical Structures with Differentiable Nondeterministic StacksCode1
Frustratingly Simple Pretraining Alternatives to Masked Language ModelingCode1
Imposing Relation Structure in Language-Model Embeddings Using Contrastive LearningCode1
-former: Infinite Memory TransformerCode1
ReMeDi: Resources for Multi-domain, Multi-service, Medical DialoguesCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect ExtractionCode1
Sentence Bottleneck Autoencoders from Transformer Language ModelsCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified