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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13051–13100 of 17610 papers

TitleStatusHype
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERCode1
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Sentence Bottleneck Autoencoders from Transformer Language ModelsCode1
How Does Adversarial Fine-Tuning Benefit BERT?—0
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot LearnersCode1
Want To Reduce Labeling Cost? GPT-3 Can HelpCode1
Selective Differential Privacy for Language ModelingCode1
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
The effects of data size on Automated Essay Scoring engines—0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching—0
Dealing with Typos for BERT-based Passage Retrieval and RankingCode1
Improving callsign recognition with air-surveillance data in air-traffic communication—0
Injecting Text in Self-Supervised Speech Pretraining—0
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Semantic-Based Self-Critical Training For Question GenerationCode1
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network—0
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of TokensCode0
SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionCode1
Detection of Criminal Texts for the Polish State Border Guard—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
Reducing Exposure Bias in Training Recurrent Neural Network Transducers—0
Taming the Beast: Learning to Control Neural Conversational Models—0
Prompt-Learning for Fine-Grained Entity Typing—0
UzBERT: pretraining a BERT model for Uzbek—0
From Two to One: A New Scene Text Recognizer with Visual Language Modeling NetworkCode1
cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSECode0
Knowledge Perceived Multi-modal Pretraining in E-commerceCode1
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text MiningCode1
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User ProfilesCode1
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You NeedCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes—0
Language Model Augmented Relevance Score—0
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification—0
Deduplicating Training Data Makes Language Models Better—0
0.8% Nyquist computational ghost imaging via non-experimental deep learning—0
A Weakly Supervised Dataset of Fine-Grained Emotions in PortugueseCode0
Modeling Protein Using Large-scale Pretrain Language ModelCode1
Scaling Laws for Deep Learning—0
Deep Natural Language Processing for LinkedIn Search—0
Autoencoders as Tools for Program SynthesisCode0
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
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage RetrievalCode1
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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