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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13301–13350 of 17610 papers

TitleStatusHype
Discrete Auto-regressive Variational Attention Models for Text ModelingCode0
Algorithm to Compilation Co-design: An Integrated View of Neural Network Sparsity—0
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model—0
Scene Transformer: A unified architecture for predicting multiple agent trajectoriesCode1
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling—0
Direction is what you need: Improving Word Embedding Compression in Large Language ModelsCode1
Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021—0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
PairConnect: A Compute-Efficient MLP Alternative to Attention—0
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts—0
SAS: Self-Augmentation Strategy for Language Model Pre-trainingCode0
Is Einstein more agreeable and less neurotic than Hitler? A computational exploration of the emotional and personality profiles of historical persons—0
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden UnitsCode1
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
Incorporating External POS Tagger for Punctuation RestorationCode1
Predicting the Ordering of Characters in Japanese Historical Documents—0
BioELECTRA:Pretrained Biomedical text Encoder using DiscriminatorsCode1
Leveraging Pre-trained Language Model for Speech Sentiment Analysis—0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition—0
MST: Masked Self-Supervised Transformer for Visual Representation—0
Auto-tagging of Short Conversational Sentences using Natural Language Processing MethodsCode0
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching—0
Hash Layers For Large Sparse Models—0
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared HypernetworksCode1
Staircase Attention for Recurrent Processing of SequencesCode1
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language ModelCode1
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision MakingCode0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
Generating Hypothetical Events for Abductive InferenceCode0
Video Imprint—0
Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning—0
Pre-trained Language Model for Web-scale Retrieval in Baidu Search—0
Top-KAST: Top-K Always Sparse TrainingCode1
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models—0
Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction—0
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues—0
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation—0
A Targeted Assessment of Incremental Processing in Neural LanguageModels and HumansCode0
Extracting Weighted Automata for Approximate Minimization in Language Modelling—0
BERTnesia: Investigating the capture and forgetting of knowledge in BERTCode0
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings—0
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsCode1
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge NotesCode1
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition—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