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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 23012350 of 17610 papers

TitleStatusHype
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
Inductive Entity Representations from Text via Link PredictionCode1
Inference with Reference: Lossless Acceleration of Large Language ModelsCode1
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-TrainingCode1
Instruction-Driven Game Engines on Large Language ModelsCode1
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out StrategiesCode1
Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS ScoringCode1
Incorporating External POS Tagger for Punctuation RestorationCode1
In-context Pretraining: Language Modeling Beyond Document BoundariesCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Incorporating Large Language Models into Production Systems for Enhanced Task Automation and FlexibilityCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene UnderstandingCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage RetrievalCode1
In-Context Learning with Many Demonstration ExamplesCode1
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout GroupsCode1
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
In-context Autoencoder for Context Compression in a Large Language ModelCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language GuidanceCode1
CriticEval: Evaluating Large Language Model as CriticCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
A context-aware knowledge transferring strategy for CTC-based ASRCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human AnnotationCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery ModelsCode1
A Probabilistic Formulation of Unsupervised Text Style TransferCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Improving Transformer Optimization Through Better InitializationCode1
3D Visual Illusion Depth EstimationCode1
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse DataCode1
CrAM: A Compression-Aware MinimizerCode1
Improving Transformer Optimization Through Better InitializationCode1
Improving Visual Grounding by Encouraging Consistent Gradient-based ExplanationsCode1
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android BytecodeCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
CDLM: Cross-Document Language ModelingCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
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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