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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1435114400 of 17610 papers

TitleStatusHype
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding0
Improving Indonesian Text Classification Using Multilingual Language ModelCode1
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition ExtractionCode1
Dialogue-adaptive Language Model Pre-training From Quality EstimationCode0
RadLex Normalization in Radiology Reports0
Pay Attention when RequiredCode0
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge GraphsCode0
Comparative Study of Language Models on Cross-Domain Data with Model Agnostic Explainability0
Brown University at TREC Deep Learning 20190
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling0
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective FunctionCode1
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model0
Improving Language Generation with Sentence Coherence ObjectiveCode0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce0
Generative Language Modeling for Automated Theorem Proving0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model0
Bio-inspired Structure Identification in Language Embeddings0
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling0
An exploratory study of L1-specific non-words0
Exploring Disparate Language Model Combination Strategies for Mandarin-English Code-Switching ASR0
A Study on Contextualized Language Modeling for FAQ Retrieval0
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim ClassificationCode1
Intermediate Training of BERT for Product MatchingCode1
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequencesCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Knowledge Efficient Deep Learning for Natural Language Processing0
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning inLanguage using neuro-symbolic cloud computingCode0
Language Models as Emotional Classifiers for Textual Conversations0
GREEK-BERT: The Greeks visiting Sesame StreetCode1
Entity and Evidence Guided Relation Extraction for DocRED0
Automatic Speech Summarisation: A Scoping Review0
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization0
Analysis and Evaluation of Language Models for Word Sense DisambiguationCode1
Conceptualized Representation Learning for Chinese Biomedical Text MiningCode0
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation MetricsCode0
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus0
Quantum Language Model with Entanglement Embedding for Question Answering0
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach0
Discovering Useful Sentence Representations from Large Pretrained Language Models0
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering0
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information0
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation0
Glancing Transformer for Non-Autoregressive Neural Machine TranslationCode1
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size0
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical InvestigationCode0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified