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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 34513500 of 17610 papers

TitleStatusHype
Construction Repetition Reduces Information Rate in DialogueCode1
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Content-based Controls For Music Large Language ModelingCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language ModelCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Regularizing and Optimizing LSTM Language ModelsCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in RecommendationCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Context-aware Decoding Reduces Hallucination in Query-focused SummarizationCode1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Contrastive Learning for Prompt-Based Few-Shot Language LearnersCode1
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game ModelsCode1
Representation Deficiency in Masked Language ModelingCode1
Context-aware Stand-alone Neural Spelling CorrectionCode1
[Re] Rigging the Lottery: Making All Tickets WinnersCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
Residual Shuffle-Exchange Networks for Fast Processing of Long SequencesCode1
Residual vector quantization for KV cache compression in large language modelCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
Rethinking Model Ensemble in Transfer-based Adversarial AttacksCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion ScaleCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
Contrastive Chain-of-Thought PromptingCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
Retrofitting Temporal Graph Neural Networks with TransformerCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Revisiting Challenges in Data-to-Text Generation with Fact GroundingCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective FunctionCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
Dynamic Contextualized Word EmbeddingsCode1
Show:102550
← PrevPage 70 of 353Next →

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