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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13801–13850 of 17610 papers

TitleStatusHype
Misspelling Correction with Pre-trained Contextual Language Model—0
Real-Time Optimized N-gram For Mobile Devices—0
Multitask Learning for Emotion and Personality DetectionCode1
Self Supervision for Attention NetworksCode0
edATLAS: An Efficient Disambiguation Algorithm for Texting in Languages with Abugida Scripts—0
Domain-aware Neural Language Models for Speech Recognition—0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsingCode1
Outline to Story: Fine-grained Controllable Story Generation from Cascaded EventsCode1
Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNsCode0
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense GenerationCode1
On-the-Fly Attention Modulation for Neural Generation—0
CDLM: Cross-Document Language ModelingCode1
Modeling Disclosive Transparency in NLP Application DescriptionsCode0
The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Prefix-Tuning: Optimizing Continuous Prompts for GenerationCode3
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative TransformersCode1
Sensei: Self-Supervised Sensor Name SegmentationCode0
Graphmax for Text Generation—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in BanglaCode1
WARP: Word-level Adversarial ReProgrammingCode1
Transformer-QL: A Step Towards Making Transformer Network Quadratically Large—0
Translation Memory Guided Neural Machine Translation—0
Towards Practical Second Order Optimization for Deep Learning—0
Neural spatio-temporal reasoning with object-centric self-supervised learning—0
Task-Agnostic and Adaptive-Size BERT Compression—0
Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
BROS: A Pre-trained Language Model for Understanding Texts in Document—0
Contextual Knowledge Distillation for Transformer Compression—0
Context-Aware Temperature for Language Modeling—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
Adding Recurrence to Pretrained Transformers—0
Discovering Autoregressive Orderings with Variational InferenceCode1
Block Skim Transformer for Efficient Question Answering—0
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATIONCode1
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration—0
Pretrain Knowledge-Aware Language Models—0
Memory Representation in Transformer—0
TaskSet: A Dataset of Optimization TasksCode0
Learning Chess Blindfolded—0
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training—0
Synthesizer: Rethinking Self-Attention for Transformer Models—0
Subformer: A Parameter Reduced Transformer—0
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS—0
Non-iterative Parallel Text Generation via Glancing Transformer—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
ROMUL: Scale Adaptative Population Based Training—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