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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 22512300 of 17610 papers

TitleStatusHype
Are Large Pre-Trained Language Models Leaking Your Personal Information?Code1
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout GroupsCode1
In-context Autoencoder for Context Compression in a Large Language ModelCode1
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation MethodCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Improving Visual Grounding by Encouraging Consistent Gradient-based ExplanationsCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
In-Context Learning with Many Demonstration ExamplesCode1
A Refer-and-Ground Multimodal Large Language Model for BiomedicineCode1
Are Emergent Abilities in Large Language Models just In-Context Learning?Code1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
CDLM: Cross-Document Language ModelingCode1
Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery ModelsCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Improving Transformer Optimization Through Better InitializationCode1
Improving Transformer Optimization Through Better InitializationCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
In-context Pretraining: Language Modeling Beyond Document BoundariesCode1
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Improving Neural Machine Translation Models with Monolingual DataCode1
Improving Passage Retrieval with Zero-Shot Question GenerationCode1
A Realistic Threat Model for Large Language Model JailbreaksCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
CrAM: A Compression-Aware MinimizerCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Improving NER's Performance with Massive financial corpusCode1
Improving Seq2Seq Grammatical Error Correction via Decoding InterventionsCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
ArcGPT: A Large Language Model Tailored for Real-world Archival ApplicationsCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
AgroGPT: Efficient Agricultural Vision-Language Model with Expert TuningCode1
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language ModelCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
Improving Mandarin Speech Recogntion with Block-augmented TransformerCode1
Improving Multi-Party Dialogue Discourse Parsing via Domain IntegrationCode1
Improving Spoken Language Modeling with Phoneme Classification: A Simple Fine-tuning ApproachCode1
Show:102550
← PrevPage 46 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