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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12351–12400 of 17610 papers

TitleStatusHype
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning—0
Black-Box Tuning for Language-Model-as-a-ServiceCode2
Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests—0
An Ensemble Approach to Acronym Extraction using TransformersCode0
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language frameworkCode0
Low-Rank Constraints for Fast Inference in Structured ModelsCode0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
Imagined versus Remembered Stories: Quantifying Differences in Narrative Flow—0
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation—0
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language ModelCode1
Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models—0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety—0
Submix: Practical Private Prediction for Large-Scale Language Models—0
Technology Mapping Using WebAI: The Case of 3D Printing—0
Rethinking Controllable Variational Autoencoders—0
Training and Generating Neural Networks in Compressed Weight SpaceCode0
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
TextRGNN: Residual Graph Neural Networks for Text Classification—0
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse GateCode1
Event-based clinical findings extraction from radiology reports with pre-trained language modelCode0
Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment—0
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment—0
Multi-Dialect Arabic Speech Recognition—0
CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form ExtractionCode0
Counterfactual Memorization in Neural Language Models—0
Visual Semantics Allow for Textual Reasoning Better in Scene Text RecognitionCode1
Towards more patient friendly clinical notes through language models and ontologies—0
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode1
Diformer: Directional Transformer for Neural Machine Translation—0
The Importance of the Current Input in Sequence Modeling—0
Spiral Language Modeling—0
Efficient Large Scale Language Modeling with Mixtures of Experts—0
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing—0
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking—0
AutoGraphex: Zero-shot Biomedical Definition Generation with Automatic Prompting—0
Efficient Hierarchical Domain Adaptation for Pretrained Language ModelsCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
Knowledge-Augmented Language Models for Cause-Effect Relation ClassificationCode1
DOCmT5: Document-Level Pretraining of Multilingual Language Models—0
Self-Supervised Learning for speech recognition with Intermediate layer supervisionCode1
Reconsidering the Past: Optimizing Hidden States in Language Models—0
Learning To Retrieve Prompts for In-Context LearningCode1
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription—0
UNIREX: A Unified Learning Framework for Language Model Rationale ExtractionCode1
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domainCode0
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—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