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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 41014150 of 17610 papers

TitleStatusHype
Balancing Label Quantity and Quality for Scalable ElicitationCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
Data Similarity is Not Enough to Explain Language Model PerformanceCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
MetaSC: Test-Time Safety Specification Optimization for Language ModelsCode0
An Approach for Text Steganography Based on Markov ChainsCode0
Planning with Multi-Constraints via Collaborative Language AgentsCode0
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context LearningCode0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
Misinformation Has High PerplexityCode0
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag CompetitionCode0
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley ValuesCode0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Data Noising as Smoothing in Neural Network Language ModelsCode0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation LearningCode0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text MiningCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural NetworksCode0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
Meta-Context Transformers for Domain-Specific Response GenerationCode0
Memory TransformerCode0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
BADGE: BADminton report Generation and Evaluation with LLMCode0
Memory-Efficient Adaptive OptimizationCode0
DATA: Differentiable ArchiTecture ApproximationCode0
DataChat: Prototyping a Conversational Agent for Dataset Search and VisualizationCode0
An Analysis of Neural Language Modeling at Multiple ScalesCode0
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form StoriesCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck LanguagesCode0
Monotonic Paraphrasing Improves Generalization of Language Model PromptingCode0
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationCode0
Adding A Filter Based on The Discriminator to Improve Unconditional Text GenerationCode0
MedRep: Medical Concept Representation for General Electronic Health Record Foundation ModelsCode0
Meeting Summarization with Pre-training and Clustering MethodsCode0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
MedINST: Meta Dataset of Biomedical InstructionsCode0
MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model ScoreCode0
Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language ModelsCode0
MedMine: Examining Pre-trained Language Models on Medication MiningCode0
Babysit A Language Model From Scratch: Interactive Language Learning by Trials and DemonstrationsCode0
Medical Vision-Language Pre-Training for Brain AbnormalitiesCode0
MedMobile: A mobile-sized language model with expert-level clinical capabilitiesCode0
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials ScienceCode0
DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic SpeakersCode0
Measuring Social Biases in Masked Language Models by Proxy of Prediction QualityCode0
Measuring Copyright Risks of Large Language Model via Partial Information ProbingCode0
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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