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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15351–15400 of 17610 papers

TitleStatusHype
Real Life Application of a Question Answering System Using BERT Language Model—0
Towards Accurate Text Verbalization for ASR Based on Audio Alignment—0
Multilingual Language Models for Named Entity Recognition in German and English—0
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists—0
Semantic Language Model for Tunisian Dialect—0
Repurposing Decoder-Transformer Language Models for Abstractive Summarization—0
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation—0
Behavior Gated Language Models—0
Implicit Deep Latent Variable Models for Text GenerationCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation RepresentationCode0
Pre-training A Neural Language Model Improves The Sample Efficiency of an Emergency Room Classification Model—0
Linguistic Versus Latent Relations for Modeling Coherent Flow in ParagraphsCode0
Multi-Task Learning with Language Modeling for Question Generation—0
Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level TrainingCode0
Probing Representations Learned by Multimodal Recurrent and Transformer Models—0
Adversarial Representation Learning for Text-to-Image Matching—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
FinBERT: Financial Sentiment Analysis with Pre-trained Language ModelsCode0
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature EmbeddingsCode0
uniblock: Scoring and Filtering Corpus with Unicode Block InformationCode0
Measuring Patent Claim Generation by Span Relevancy—0
Connecting and Comparing Language Model Interpolation Techniques—0
The Limitations of Stylometry for Detecting Machine-Generated Fake News—0
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings—0
Release Strategies and the Social Impacts of Language Models—0
A framework for anomaly detection using language modeling, and its applications to finance—0
Deep Learning Based Chatbot ModelsCode0
Neural Poetry: Learning to Generate Poems using Syllables—0
Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model—0
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning—0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Latent Relation Language Models—0
WikiCREM: A Large Unsupervised Corpus for Coreference ResolutionCode0
Restricted Recurrent Neural NetworksCode0
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical TextCode0
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer—0
Universal Adversarial Triggers for Attacking and Analyzing NLPCode0
Question Answering based Clinical Text Structuring Using Pre-trained Language Model—0
Encoder-Agnostic Adaptation for Conditional Language GenerationCode0
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings—0
Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions—0
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
EmotionX-IDEA: Emotion BERT -- an Affectional Model for ConversationCode0
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring—0
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training—0
Visualizing and Understanding the Effectiveness of BERT—0
SenseBERT: Driving Some Sense into BERT—0
On The Evaluation of Machine Translation Systems Trained With Back-TranslationCode0
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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