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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1295113000 of 17610 papers

TitleStatusHype
KnowMAN: Weakly Supervised Multinomial Adversarial NetworksCode1
Let the CAT out of the bag: Contrastive Attributed explanations for Text0
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance DetectionCode0
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation0
Context-NER : Contextual Phrase Generation at ScaleCode1
Do Language Models Know the Way to Rome?0
Efficient Domain Adaptation of Language Models via Adaptive Tokenization0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation0
Improving Text Auto-Completion with Next Phrase Prediction0
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-trainingCode1
Comparing Text Representations: A Theory-Driven ApproachCode0
Dialogue State Tracking with a Language Model using Schema-Driven PromptingCode1
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking0
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence RepresentationsCode1
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation SystemsCode0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation0
Tied & Reduced RNN-T Decoder0
A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence0
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks0
MDAPT: Multilingual Domain Adaptive Pretraining in a Single ModelCode0
LM-Critic: Language Models for Unsupervised Grammatical Error CorrectionCode1
Rationales for Sequential PredictionsCode1
Types of Out-of-Distribution Texts and How to Detect ThemCode1
xGQA: Cross-Lingual Visual Question AnsweringCode1
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained ModelsCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
Connecting degree and polarity: An artificial language learning studyCode0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation0
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuningCode1
Single-Read Reconstruction for DNA Data Storage Using Transformers0
TEASEL: A Transformer-Based Speech-Prefixed Language ModelCode1
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language ModelsCode0
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking0
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-TrainingCode1
Dual-State Capsule Networks for Text Classification0
Euphemistic Phrase Detection by Masked Language ModelCode1
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary InitializationCode1
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling0
Studying word order through iterative shufflingCode0
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
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
Efficient Nearest Neighbor Language ModelsCode1
Avoiding Inference Heuristics in Few-shot Prompt-based FinetuningCode1
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language ModelsCode1
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal TransformersCode1
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring0
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces0
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
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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