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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 37013750 of 17610 papers

TitleStatusHype
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language ModelingCode1
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot LearnersCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold NetworksCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
TacoLM: GaTed Attention Equipped Codec Language Model are Efficient Zero-Shot Text to Speech SynthesizersCode1
BERT got a Date: Introducing Transformers to Temporal TaggingCode1
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation TasksCode1
BERT Goes Shopping: Comparing Distributional Models for Product RepresentationsCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language ModelsCode1
Talking-Heads AttentionCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
ADCNet: a unified framework for predicting the activity of antibody-drug conjugatesCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
CDLM: Cross-Document Language ModelingCode1
Accelerating Toeplitz Neural Network with Constant-time Inference ComplexityCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
CrAM: A Compression-Aware MinimizerCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled DataCode1
Template-Based Named Entity Recognition Using BARTCode1
Enhancing Monocular 3D Scene Completion with Diffusion ModelCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
Effective Attention Sheds Light On InterpretabilityCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
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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