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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13601–13650 of 17610 papers

TitleStatusHype
Cross-Lingual Language Model Meta-Pretraining—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network—0
BFClass: A Backdoor-free Text Classification Framework—0
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder—0
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets—0
Distilling Relation Embeddings from Pre-trained Language Models—0
The Trade-offs of Domain Adaptation for Neural Language Models—0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Learning Natural Language Generation from Scratch—0
Influence of ASR and Language Model on Alzheimer's Disease Detection—0
Adversarial Training with Contrastive Learning in NLP—0
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy—0
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis—0
Language Models as a Knowledge Source for Cognitive Agents—0
Primer: Searching for Efficient Transformers for Language ModelingCode0
Relating Neural Text Degeneration to Exposure Bias—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
BART-light: One Decoder Layer Is Enough—0
Exploring Multitask Learning for Low-Resource AbstractiveSummarization—0
Does Commonsense help in detecting Sarcasm?Code0
Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification—0
A Bag of Tricks for Dialogue Summarization—0
Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning—0
Do Language Models Know the Way to Rome?—0
Let the CAT out of the bag: Contrastive Attributed explanations for Text—0
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation—0
Regularized Training of Nearest Neighbor Language Models—0
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance DetectionCode0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation—0
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking—0
Tied & Reduced RNN-T Decoder—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
Comparing Text Representations: A Theory-Driven ApproachCode0
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation SystemsCode0
Improving Text Auto-Completion with Next Phrase Prediction—0
Efficient Domain Adaptation of Language Models via Adaptive Tokenization—0
A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence—0
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks—0
MDAPT: Multilingual Domain Adaptive Pretraining in a Single ModelCode0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
Connecting degree and polarity: An artificial language learning studyCode0
Single-Read Reconstruction for DNA Data Storage Using Transformers—0
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language ModelsCode0
Studying word order through iterative shufflingCode0
Dual-State Capsule Networks for Text Classification—0
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling—0
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking—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