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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1570115750 of 17610 papers

TitleStatusHype
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking AgentCode0
On the Structural Memory of LLM AgentsCode0
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding ModelsCode0
Measuring Social Biases in Masked Language Models by Proxy of Prediction QualityCode0
SG-Net: Syntax-Guided Machine Reading ComprehensionCode0
Measuring Copyright Risks of Large Language Model via Partial Information ProbingCode0
On the State of the Art of Evaluation in Neural Language ModelsCode0
The Traitors: Deception and Trust in Multi-Agent Language Model SimulationsCode0
Tagging and parsing of multidomain collectionsCode0
Understanding Stragglers in Large Model Training Using What-if AnalysisCode0
Transfer learning from language models to image caption generators: Better models may not transfer betterCode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Measuring Contextual Informativeness in Child-Directed TextCode0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text NormalizationCode0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
MDAPT: Multilingual Domain Adaptive Pretraining in a Single ModelCode0
On the Role of Context in Reading Time PredictionCode0
Maybe Deep Neural Networks are the Best Choice for Modeling Source CodeCode0
MaxUp: A Simple Way to Improve Generalization of Neural Network TrainingCode0
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical ReasoningCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
On the Robustness of Reward Models for Language Model AlignmentCode0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank AdaptationCode0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
SharpZO: Hybrid Sharpness-Aware Vision Language Model Prompt Tuning via Forward-Only PassesCode0
Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared TaskCode0
On the Reliability of Large Language Models to Misinformed and Demographically-Informed PromptsCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
UDALM: Unsupervised Domain Adaptation through Language ModelingCode0
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error CorrectionCode0
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph CaptioningCode0
Light Coreference Resolution for Russian with Hierarchical Discourse FeaturesCode0
On the Relationship between Truth and Political Bias in Language ModelsCode0
Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question AnsweringCode0
Shifting from endangerment to rebirth in the Artificial Intelligence Age: An Ensemble Machine Learning Approach for Hawrami Text ClassificationCode0
Shifting Mean Activation Towards Zero with Bipolar Activation FunctionsCode0
SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal GroundingCode0
On the Proper Treatment of Tokenization in PsycholinguisticsCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language ReasoningCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Understanding Domain Learning in Language Models Through Subpopulation AnalysisCode0
Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical DomainCode0
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn MoreCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
Masked Latent Semantic Modeling: an Efficient Pre-training Alternative to Masked Language ModelingCode0
On the Limitations of Sociodemographic Adaptation with TransformersCode0
On the Generalization Ability of Retrieval-Enhanced TransformersCode0
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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