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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16201–16250 of 17610 papers

TitleStatusHype
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model—0
Improving Black-box Speech Recognition using Semantic Parsing—0
Can Discourse Relations be Identified Incrementally?—0
Hyperspherical Query Likelihood Models with Word Embeddings—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—0
On Modeling Sense Relatedness in Multi-prototype Word Embedding—0
Kyoto University Participation to WAT 2017Code0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel CorpusCode0
Fraternal DropoutCode0
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue ModelingCode0
Tensor network language model—0
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models—0
Rotational Unit of MemoryCode0
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGACode0
Low-Rank RNN Adaptation for Context-Aware Language ModelingCode0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Counterfactual Language Model Adaptation for Suggesting PhrasesCode0
Person Re-Identification with Vision and Language—0
Normalizador de Texto para Lingua Portuguesa baseado em Modelo de Linguagem (A Normalizer based on Language Model for Texts in Portuguese)[In Portuguese]—0
Generating Sentences by Editing PrototypesCode0
Improving Language Modelling with Noise-contrastive estimation—0
Sentence Correction Based on Large-scale Language Modelling—0
Dynamic Evaluation of Neural Sequence ModelsCode0
Neural Networks for Text Correction and Completion in Keyboard Decoding—0
Language Modeling with Highway LSTM—0
Learning Intrinsic Sparse Structures within Long Short-Term MemoryCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Language Models of Spoken Dutch—0
Shifting Mean Activation Towards Zero with Bipolar Activation FunctionsCode0
Cynical Selection of Language Model Training DataCode0
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment—0
Understanding the Logical and Semantic Structure of Large Documents—0
Patterns versus Characters in Subword-aware Neural Language ModelingCode0
Memory Augmented Neural Networks for Natural Language Processing—0
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings—0
Revising the METU-Sabanc Turkish Treebank: An Exercise in Surface-Syntactic Annotation of Agglutinative Languages—0
What are the limitations on the flux of syntactic dependencies? Evidence from UD treebanks—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
UHH Submission to the WMT17 Quality Estimation Shared Task—0
University of Rochester WMT 2017 NMT System Submission—0
What do we need to know about an unknown word when parsing German—0
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
Character-based recurrent neural networks for morphological relational reasoning—0
Generating Image Descriptions using Multilingual Data—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