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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1300113050 of 17610 papers

TitleStatusHype
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations0
Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement0
Extended Parallel Corpus for Amharic-English Machine Translation0
Extended Study on Using Pretrained Language Models and YiSi-1 for Machine Translation Evaluation0
Extended Translation Models in Phrase-based Decoding0
Extending Memory for Language Modelling0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin0
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)0
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results0
Extensible Embedding: A Flexible Multipler For LLM's Context Length0
Extensible Prompts for Language Models on Zero-shot Language Style Customization0
External Knowledge Augmented Polyphone Disambiguation Using Large Language Model0
External Language Model Integration for Factorized Neural Transducers0
Extracting Adverse Drug Events from Clinical Notes0
Extracting and Inferring Personal Attributes from Dialogue0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT0
Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures0
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context0
Extracting chemical food safety hazards from the scientific literature automatically using large language models0
Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows0
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking0
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study0
Extracting Semantic Process Information from the Natural Language in Event Logs0
Extracting Semantics from Maintenance Records0
Extracting Social Networks from Literary Text with Word Embedding Tools0
Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model0
Extracting Spatiotemporal Data from Gradients with Large Language Models0
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-30
Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods0
Extracting Weighted Automata for Approximate Minimization in Language Modelling0
Extracting Weighted Language Lexicons from Wikipedia0
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation0
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing0
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News0
Extractive Summarisation Based on Keyword Profile and Language Model0
Extractive Summary as Discrete Latent Variables0
Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback0
Extrapolating Multilingual Understanding Models as Multilingual Generators0
Extremely Small BERT Models from Mixed-Vocabulary Training0
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes0
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation0
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction0
FAC^2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition0
FaceInsight: A Multimodal Large Language Model for Face Perception0
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection0
Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments0
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring0
Facilitating Video Story Interaction with Multi-Agent Collaborative System0
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation0
Show:102550
← PrevPage 261 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