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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1350113550 of 17610 papers

TitleStatusHype
BERT-CoQAC: BERT-based Conversational Question Answering in Context0
Transfer training from smaller language model0
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network0
Extracting Adverse Drug Events from Clinical Notes0
On Sampling-Based Training Criteria for Neural Language Modeling0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning0
Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?Code1
Adapting Long Context NLM for ASR Rescoring in Conversational Agents0
Improving Biomedical Pretrained Language Models with KnowledgeCode1
Differentiable Model Compression via Pseudo Quantization NoiseCode1
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language ModelCode1
B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc RetrievalCode0
ELECTRAMed: a new pre-trained language representation model for biomedical NLPCode1
BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian0
When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional WeightingCode0
Operationalizing a National Digital Library: The Case for a Norwegian Transformer ModelCode1
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training0
Go Forth and Prosper: Language Modeling with Ancient Textual HistoryCode0
Misinfo Reaction Frames: Reasoning about Readers' Reactions to News HeadlinesCode1
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
Towards Open-World Text-Guided Face Image Generation and ManipulationCode1
On the Influence of Masking Policies in Intermediate Pre-training0
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal ConversationsCode1
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLPCode1
Explaining Answers with Entailment TreesCode1
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema0
Enriching a Model's Notion of Belief using a Persistent Memory0
Text2App: A Framework for Creating Android Apps from Text DescriptionsCode1
Probing Across Time: What Does RoBERTa Know and When?Code1
Condenser: a Pre-training Architecture for Dense RetrievalCode1
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social MediaCode1
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
A Masked Segmental Language Model for Unsupervised Natural Language SegmentationCode0
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic EmbeddingsCode0
Rethinking Text Line Recognition Models0
Time-Stamped Language Model: Teaching Language Models to Understand the Flow of EventsCode1
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language ModelsCode0
Natural Language Understanding with Privacy-Preserving BERT0
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian0
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval0
How to Train BERT with an Academic BudgetCode1
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation ExtractionCode1
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding0
Bilingual alignment transfers to multilingual alignment for unsupervised parallel text miningCode0
Mean-Squared Accuracy of Good-Turing Estimator0
UDALM: Unsupervised Domain Adaptation through Language ModelingCode0
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding LearningCode1
IGA : An Intent-Guided Authoring AssistantCode0
Event Detection as Question Answering with Entity InformationCode0
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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