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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14851–14900 of 17610 papers

TitleStatusHype
A Financial Service Chatbot based on Deep Bidirectional Transformers—0
Global and Local Feature Learning for Ego-Network Analysis—0
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and GenerationCode1
Transformer on a DietCode1
A Data Efficient End-To-End Spoken Language Understanding Architecture—0
FQuAD: French Question Answering Dataset—0
CBAG: Conditional Biomedical Abstract Generation—0
Comparison of Turkish Word Representations Trained on Different Morphological Forms—0
Deep Learning for Source Code Modeling and Generation: Models, Applications and ChallengesCode0
Regularizing activations in neural networks via distribution matching with the Wasserstein metric—0
Pre-Training for Query Rewriting in A Spoken Language Understanding System—0
Learning Cross-modal Context Graph for Visual GroundingCode1
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning—0
REALM: Retrieval-Augmented Language Model Pre-TrainingCode1
How Much Knowledge Can You Pack Into the Parameters of a Language Model?Code1
A Probabilistic Formulation of Unsupervised Text Style TransferCode1
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-ExpertsCode1
FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA—0
Limits of Detecting Text Generated by Large-Scale Language Models—0
Time-aware Large Kernel ConvolutionsCode1
Blank Language ModelsCode1
Snippext: Semi-supervised Opinion Mining with Augmented DataCode1
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
Parsing as PretrainingCode1
A Difference-of-Convex Programming Approach With Parallel Branch-and-Bound For Sentence Compression Via A Hybrid Extractive Model—0
Explaining Relationships Between Scientific DocumentsCode1
Adversarial Training for Aspect-Based Sentiment Analysis with BERTCode1
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
DUMA: Reading Comprehension with Transposition ThinkingCode1
Compressing Language Models using Doped Kronecker Products—0
Reducing Non-Normative Text Generation from Language Models—0
Scaling Laws for Neural Language ModelsCode1
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine TranslationCode1
ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data—0
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on GeneralizationCode1
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceCode1
Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue SystemsCode1
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard—0
RobBERT: a Dutch RoBERTa-based Language ModelCode1
Block-wise Dynamic SparsenessCode0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
Montage: A Neural Network Language Model-Guided JavaScript Engine FuzzerCode1
Reformer: The Efficient TransformerCode2
Revisiting Challenges in Data-to-Text Generation with Fact GroundingCode1
A Continuous Space Neural Language Model for Bengali Language—0
Learning Cross-Context Entity Representations from Text—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