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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 15001–15050 of 17610 papers

TitleStatusHype
Pre-Training for Query Rewriting in A Spoken Language Understanding System—0
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning—0
Limits of Detecting Text Generated by Large-Scale Language Models—0
FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA—0
Introducing Aspects of Creativity in Automatic Poetry GenerationCode0
Consistency of a Recurrent Language Model With Respect to Incomplete DecodingCode0
Aligning the Pretraining and Finetuning Objectives of Language Models—0
A Difference-of-Convex Programming Approach With Parallel Branch-and-Bound For Sentence Compression Via A Hybrid Extractive Model—0
Aspect-based Academic Search using Domain-specific KB—0
Joint Contextual Modeling for ASR Correction and Language Understanding—0
PEL-BERT: A Joint Model for Protocol Entity Linking—0
Compressing Language Models using Doped Kronecker Products—0
Reducing Non-Normative Text Generation from Language Models—0
ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data—0
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard—0
Block-wise Dynamic SparsenessCode0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
A Continuous Space Neural Language Model for Bengali Language—0
Learning Cross-Context Entity Representations from Text—0
Towards Minimal Supervision BERT-based Grammar Error Correction—0
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study—0
Transformer-based language modeling and decoding for conversational speech recognition—0
On the comparability of Pre-trained Language Models—0
Incremental Monoidal Grammars—0
FedBoost: A Communication-Efficient Algorithm for Federated Learning—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
Retrieval Augmented Language Model Pre-Training—0
Script knowledge constrains ellipses in fragments – Evidence from production data and language modeling—0
PoKED: A Semi-Supervised System for Word Sense Disambiguation—0
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments—0
Pseudo-Masked Language Models for Unified Language Model Pre-Training—0
oLMpics -- On what Language Model Pre-training CapturesCode0
"Hinglish" Language -- Modeling a Messy Code-Mixed Language—0
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models—0
Encoding word order in complex embeddingsCode0
Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-AttentionCode0
Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction—0
Explicit Sparse Transformer: Concentrated Attention Through Explicit SelectionCode0
Falcon 2.0: An Entity and Relation Linking Tool over WikidataCode0
end-to-end training of a large vocabulary end-to-end speech recognition system—0
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture—0
Recurrent Hierarchical Topic-Guided RNN for Language GenerationCode0
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model—0
Shareable Representations for Search Query Understanding—0
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language ModelsCode0
Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural NetworksCode0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
Analyzing Information Leakage of Updates to Natural Language Models—0
KARL: Knowledge-Aware Reasoning Memory Modeling with Reinforcement Learning of Vector Space—0
Recurrent Highway Networks with Grouped Auxiliary MemoryCode0
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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