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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 60516100 of 17610 papers

TitleStatusHype
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case StudyCode0
Simple Fusion: Return of the Language ModelCode0
LIBRA: Measuring Bias of Large Language Model from a Local ContextCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language ModelCode0
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
Multi-Programming Language Ensemble for Code Generation in Large Language ModelCode0
Memory TransformerCode0
Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at RuntimeCode0
Ordered Neurons: Integrating Tree Structures into Recurrent Neural NetworksCode0
Neural Machine Translation in Linear TimeCode0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Neural Machine Translation For Low Resource LanguagesCode0
Simple Unsupervised Summarization by Contextual MatchingCode0
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information ExtractionCode0
Memory-Efficient Adaptive OptimizationCode0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment AnalysisCode0
Neural Linguistic SteganographyCode0
keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span DetectionCode0
Simplifying Scholarly Abstracts for Accessible Digital LibrariesCode0
LICHEE: Improving Language Model Pre-training with Multi-grained TokenizationCode0
NoCoLA: The Norwegian Corpus of Linguistic AcceptabilityCode0
Optimizing Retrieval-augmented Reader Models via Token EliminationCode0
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias BenchmarksCode0
Sig-Networks Toolkit: Signature Networks for Longitudinal Language ModellingCode0
Sig2text, a Vision-language model for Non-cooperative Radar Signal ParsingCode0
Training Vision-Language Models with Less Bimodal SupervisionCode0
LLM Safety Alignment is Divergence Estimation in DisguiseCode0
Siamese-DETR for Generic Multi-Object TrackingCode0
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck LanguagesCode0
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific RewardsCode0
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence LearningCode0
OTCE: Hybrid SSM and Attention with Cross Domain Mixture of Experts to construct Observer-Thinker-Conceiver-ExpresserCode0
The Traitors: Deception and Trust in Multi-Agent Language Model SimulationsCode0
Making the Most of Text Semantics to Improve Biomedical Vision--Language ProcessingCode0
PromptCL: Improving Event Representation via Prompt Template and Contrastive LearningCode0
A Comparison of Language Modeling and Translation as Multilingual Pretraining ObjectivesCode0
Single Headed Attention RNN: Stop Thinking With Your HeadCode0
Language Model Alignment with Elastic ResetCode0
Revisiting Few-Shot Object Detection with Vision-Language ModelsCode0
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model InferenceCode0
LG-CAV: Train Any Concept Activation Vector with Language GuidanceCode0
Neural Lattice Language ModelsCode0
SJ_AJ@DravidianLangTech-EACL2021: Task-Adaptive Pre-Training of Multilingual BERT models for Offensive Language IdentificationCode0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Language Models as Context-sensitive Word Search EnginesCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Show and Guide: Instructional-Plan Grounded Vision and Language ModelCode0
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error CorrectionCode0
Show:102550
← PrevPage 122 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