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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10351–10400 of 17610 papers

TitleStatusHype
'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution—0
Neural Knowledge Bank for Pretrained Transformers—0
Pre-training A Neural Language Model Improves The Sample Efficiency of an Emergency Room Classification Model—0
Neural Language Modeling for Named Entity Recognition—0
Neural Language Modeling with Visual Features—0
Neural Language Model Pruning for Automatic Speech Recognition—0
Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps—0
Neural language models for text classification in evidence-based medicine—0
Neural Machine Translation for Cross-Lingual Pronoun Prediction—0
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search—0
Neural Machine Translation Models Can Learn to be Few-shot Learners—0
Neural Machine Translation with Decoding History Enhanced Attention—0
Neural Memory Plasticity for Anomaly Detection—0
Neural Models for Predicting Celtic Mutations—0
Neural Models for Source Code Synthesis and Completion—0
Neural Multitask Learning for Simile Recognition—0
Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation—0
Neural Network Language Model for Chinese Pinyin Input Method Engine—0
Neural Network Language Modeling with Letter-based Features and Importance Sampling—0
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation—0
Neural networks based EEG-Speech Models—0
Neural Networks Compression for Language Modeling—0
Neural Networks for Text Correction and Completion in Keyboard Decoding—0
Neural Network Transduction Models in Transliteration Generation—0
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile—0
Neural Poetry: Learning to Generate Poems using Syllables—0
Neural Polysynthetic Language Modelling—0
Neural Predictive Text for Grammatical Error Prevention—0
Neural Probabilistic Language Model for System Combination—0
Neural Random Projections for Language Modelling—0
Neural Search Space in Gboard Decoder—0
Neural Sequence-to-sequence Learning of Internal Word Structure—0
Neural Sparse Topical Coding—0
Neural spatio-temporal reasoning with object-centric self-supervised learning—0
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition—0
Neural Summarization of Electronic Health Records—0
Neural Syntactic Generative Models with Exact Marginalization—0
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis—0
Neural Thermodynamic Laws for Large Language Model Training—0
Neural Transition-based Syntactic Linearization—0
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
NeurIPS 2023 LLM Efficiency Fine-tuning Competition—0
NeuroGen: Neural Network Parameter Generation via Large Language Models—0
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization—0
Neuron Patching: Semantic-based Neuron-level Language Model Repair for Code Generation—0
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation—0
NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis—0
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network—0
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions—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