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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13701–13750 of 17610 papers

TitleStatusHype
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of TokensCode0
Reducing Exposure Bias in Training Recurrent Neural Network Transducers—0
Prompt-Learning for Fine-Grained Entity Typing—0
Taming the Beast: Learning to Control Neural Conversational Models—0
Detection of Criminal Texts for the Polish State Border Guard—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
UzBERT: pretraining a BERT model for Uzbek—0
cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSECode0
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes—0
Language Model Augmented Relevance Score—0
Scaling Laws for Deep Learning—0
A Weakly Supervised Dataset of Fine-Grained Emotions in PortugueseCode0
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification—0
0.8% Nyquist computational ghost imaging via non-experimental deep learning—0
Deduplicating Training Data Makes Language Models Better—0
Autoencoders as Tools for Program SynthesisCode0
Deep Natural Language Processing for LinkedIn Search—0
Caption Generation on Scenes with Seen and Unseen Object Categories—0
Towards Structured Dynamic Sparse Pre-Training of BERT—0
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answering—0
Perturbing Inputs for Fragile Interpretations in Deep Natural Language ProcessingCode0
Extracting Semantics from Maintenance Records—0
A Transformer-based Math Language Model for Handwritten Math Expression Recognition—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease DiagnosisCode0
IntenT5: Search Result Diversification using Causal Language Models—0
Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classificationCode0
Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims—0
Language Model Evaluation in Open-ended Text Generation—0
StrucTexT: Structured Text Understanding with Multi-Modal TransformersCode0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—0
Towards Zero-shot Language Modeling—0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching—0
Sentence Semantic Regression for Text Generation—0
Deriving Disinformation Insights from Geolocalized Twitter CalloutsCode0
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention—0
Curriculum learning for language modelingCode0
Mitigating harm in language models with conditional-likelihood filtration—0
Large-Scale Differentially Private BERT—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech ProcessingCode0
Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning—0
LICHEE: Improving Language Model Pre-training with Multi-grained TokenizationCode0
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text RecognitionCode0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus—0
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification—0
Small-Scale Cross-Language Authorship Attribution on Social Media Comments—0
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation—0
SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task—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