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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 15951–16000 of 17610 papers

TitleStatusHype
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR—0
Neural Sparse Topical Coding—0
The Influence of Context on Sentence Acceptability JudgementsCode0
Sampling Informative Training Data for RNN Language Models—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories—0
Pretraining Sentiment Classifiers with Unlabeled Dialog Data—0
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better—0
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers—0
Subword-level Word Vector Representations for KoreanCode0
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data—0
Learning-based Composite Metrics for Improved Caption Evaluation—0
A Neural Approach to Pun Generation—0
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation—0
A Language Model based Evaluator for Sentence Compression—0
Document Modeling with External Attention for Sentence ExtractionCode0
GNEG: Graph-Based Negative Sampling for word2vec—0
Connecting Language and Vision to Actions—0
Word Error Rate Estimation for Speech Recognition: e-WERCode1
Translating a Language You Don't Know In the Chinese Room—0
Peerus Review: a tool for scientific experts finding—0
Contextual Language Model Adaptation for Conversational Agents—0
Handling Massive N-Gram Datasets EfficientlyCode0
DARTS: Differentiable Architecture SearchCode1
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
Evaluation of sentence embeddings in downstream and linguistic probing tasksCode0
Deep Lip Reading: a comparison of models and an online application—0
Semantic Variation in Online Communities of Practice—0
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks—0
On Accurate Evaluation of GANs for Language Generation—0
Improving latent variable descriptiveness with AutoGen—0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages—0
Improving Language Understanding by Generative Pre-TrainingCode1
Finding Syntax in Human Encephalography with Beam Search—0
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models—0
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers—0
Are All Languages Equally Hard to Language-Model?—0
Relational recurrent neural networksCode0
Self-Normalization Properties of Language Modeling—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
Improving neural morphological Tagging using Language Models—0
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units—0
Neural Sign Language TranslationCode0
Making Convolutional Networks Recurrent for Visual Sequence Learning—0
SB@GU at the Complex Word Identification 2018 Shared Task—0
Language Model Based Grammatical Error Correction without Annotated Training Data—0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Show:102550
← PrevPage 320 of 353Next →
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