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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17051–17100 of 17610 papers

TitleStatusHype
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning—0
Deep API Learning—0
Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce—0
Deep Bidirectional Transformers for Relation Extraction without Supervision—0
DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods—0
Deep Contextual Embeddings for Address Classification in E-commerce—0
Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings—0
Deep dive into CoCon - A Self Supervised approach for Controlled Text Generation—0
DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning—0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Deep Graph Kernels—0
DeepGreen: Effective LLM-Driven Green-washing Monitoring System Designed for Empirical Testing -- Evidence from China—0
DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning—0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning—0
Deep Image Style Transfer from Freeform Text—0
DeepJoin: Joinable Table Discovery with Pre-trained Language Models—0
Deep Learning and Continuous Representations for Natural Language Processing—0
Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic—0
Deep Learning for Bias Detection: From Inception to Deployment—0
Deep learning for music generation. Four approaches and their comparative evaluation—0
Deep Learning for NLP (without Magic)—0
Deep learning languages: a key fundamental shift from probabilities to weights?—0
Deep Learning Scaling is Predictable, Empirically—0
Deep Lexical Hypothesis: Identifying personality structure in natural language—0
Deep Lip Reading: a comparison of models and an online application—0
DeepLocalization: Using change point detection for Temporal Action Localization—0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
Deep Markov Neural Network for Sequential Data Classification—0
DeepMet: A Reading Comprehension Paradigm for Token-level Metaphor Detection—0
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media—0
DeepMutants: Training neural bug detectors with contextual mutations—0
Deep Natural Language Processing for LinkedIn Search—0
Deep Neural Compression Via Concurrent Pruning and Self-Distillation—0
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature—0
Deep Neural Network Language Models—0
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation—0
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition—0
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity—0
Deep RNNs Encode Soft Hierarchical Syntax—0
DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing—0
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs—0
Deep Structured Output Learning for Unconstrained Text Recognition—0
DeepTitle -- Leveraging BERT to generate Search Engine Optimized Headlines—0
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR—0
Deep Visual Analogy-Making—0
Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model—0
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression—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