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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 14651–14700 of 17610 papers

TitleStatusHype
RadLex Normalization in Radiology Reports—0
Pay Attention when RequiredCode0
Comparative Study of Language Models on Cross-Domain Data with Model Agnostic Explainability—0
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge GraphsCode0
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling—0
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
Brown University at TREC Deep Learning 2019—0
Improving Language Generation with Sentence Coherence ObjectiveCode0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—0
Generative Language Modeling for Automated Theorem Proving—0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—0
Bio-inspired Structure Identification in Language Embeddings—0
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
An exploratory study of L1-specific non-words—0
Exploring Disparate Language Model Combination Strategies for Mandarin-English Code-Switching ASR—0
A Study on Contextualized Language Modeling for FAQ Retrieval—0
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
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning inLanguage using neuro-symbolic cloud computingCode0
Knowledge Efficient Deep Learning for Natural Language Processing—0
Language Models as Emotional Classifiers for Textual Conversations—0
Automatic Speech Summarisation: A Scoping Review—0
Entity and Evidence Guided Relation Extraction for DocRED—0
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization—0
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 Corpus—0
Quantum Language Model with Entanglement Embedding for Question Answering—0
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift—0
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering—0
Discovering Useful Sentence Representations from Large Pretrained Language Models—0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach—0
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information—0
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation—0
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size—0
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical InvestigationCode0
Adaptable Multi-Domain Language Model for Transformer ASR—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit—0
Transformer with Bidirectional Decoder for Speech Recognition—0
FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire—0
Efficient Neural Query Auto Completion—0
Efficient MDI Adaptation for n-gram Language Models—0
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation—0
Learning Visual Representations with Caption Annotations—0
On Learning Universal Representations Across Languages—0
Language Modelling for Source Code with Transformer-XLCode0
Show:102550
← PrevPage 294 of 353Next →
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