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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1600116050 of 17610 papers

TitleStatusHype
Multi-task Pre-training Language Model for Semantic Network CompletionCode0
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized RepresentationsCode0
TNT-KID: Transformer-based Neural Tagger for Keyword IdentificationCode0
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-TrainingCode0
Neural Linguistic SteganographyCode0
TexShape: Information Theoretic Sentence Embedding for Language ModelsCode0
Low-Resource Language Modelling of South African LanguagesCode0
Neural Lattice Language ModelsCode0
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model AlignmentCode0
SPACE-IDEAS: A Dataset for Salient Information Detection in Space InnovationCode0
Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort DiscoveryCode0
Neural Language Modeling by Jointly Learning Syntax and LexiconCode0
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor DetectionCode0
To Adapt or to Fine-tune: A Case Study on Abstractive SummarizationCode0
Span Selection Pre-training for Question AnsweringCode0
Low-Rank RNN Adaptation for Context-Aware Language ModelingCode0
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer LearningCode0
Towards the Generation of Musical Explanations with GPT-3Code0
Text-based classification of interviews for mental health -- juxtaposing the state of the artCode0
Low-rank passthrough neural networksCode0
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in JapaneseCode0
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence LabelerCode0
Neural Generation for Czech: Data and BaselinesCode0
Xmodel-2 Technical ReportCode0
uniblock: Scoring and Filtering Corpus with Unicode Block InformationCode0
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource LanguagesCode0
UniDetox: Universal Detoxification of Large Language Models via Dataset DistillationCode0
Unified Language Model Pre-training for Natural Language Understanding and GenerationCode0
Unified Lexical Representation for Interpretable Visual-Language AlignmentCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
Unified Modeling Language Code Generation from Diagram Images Using Multimodal Large Language ModelsCode0
Unified Representation for Non-compositional and Compositional ExpressionsCode0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Unifying Global and Local Scene Entities Modelling for Precise Action SpottingCode0
Unifying Visual-Semantic Embeddings with Multimodal Neural Language ModelsCode0
UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan DatabaseCode0
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment ClassificationCode0
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words ModelCode0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Universal Adversarial Triggers for Attacking and Analyzing NLPCode0
Universal Language Model Fine-Tuning with Subword Tokenization for PolishCode0
Universal TransformersCode0
Unknown Script: Impact of Script on Cross-Lingual TransferCode0
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem SolvingCode0
Unlocking Efficiency: Adaptive Masking for Gene Transformer ModelsCode0
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue SystemCode0
Unraveling Feature Extraction Mechanisms in Neural NetworksCode0
Unsupervised Abstractive Summarization of Bengali Text DocumentsCode0
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence LabelingCode0
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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