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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 20012050 of 17610 papers

TitleStatusHype
Learning diverse attacks on large language models for robust red-teaming and safety tuningCode1
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
Enhancing Dialogue Generation via Dynamic Graph Knowledge AggregationCode1
InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User InstructionsCode1
Learning Hierarchical Structures with Differentiable Nondeterministic StacksCode1
Enhancing Domain Adaptation through Prompt Gradient AlignmentCode1
Knowledge Rumination for Pre-trained Language ModelsCode1
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language ModelsCode1
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental LearningCode1
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in BanglaCode1
Enhancing Indic Handwritten Text Recognition Using Global Semantic InformationCode1
CodeQueries: A Dataset of Semantic Queries over CodeCode1
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in BanglaCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language ModelsCode1
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language ModelsCode1
Hybrid Ranking Network for Text-to-SQLCode1
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation PerturbationCode1
CAREER: A Foundation Model for Labor Sequence DataCode1
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific ApplicationsCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate SpeechCode1
Length Generalization of Causal Transformers without Position EncodingCode1
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
Enhancing Vision-Language Model with Unmasked Token AlignmentCode1
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak DecoderCode1
Batch Prompting: Efficient Inference with Large Language Model APIsCode1
Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion InferenceCode1
Human Language ModelingCode1
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode1
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt DependenceCode1
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed HypergraphsCode1
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical AnalysisCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text DataCode1
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden UnitsCode1
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image TranslationCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving SequencesCode1
Lexicon-Level Contrastive Visual-Grounding Improves Language ModelingCode1
Bayesian Recurrent Neural NetworksCode1
Epidemic Modeling with Generative AgentsCode1
Bayesian Sparsification of Recurrent Neural NetworksCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
CriticEval: Evaluating Large Language Model as CriticCode1
CDLM: Cross-Document Language ModelingCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
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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