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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 51015150 of 17610 papers

TitleStatusHype
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language ModelCode0
CIE: Controlling Language Model Text Generations Using Continuous SignalsCode0
APPLS: Evaluating Evaluation Metrics for Plain Language SummarizationCode0
Applying a Pre-trained Language Model to Spanish Twitter Humor PredictionCode0
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language modelsCode0
IFShip: Interpretable Fine-grained Ship Classification with Domain Knowledge-Enhanced Vision-Language ModelsCode0
IGA : An Intent-Guided Authoring AssistantCode0
An Effective Domain Adaptive Post-Training Method for BERT in Response SelectionCode0
IgboBERT Models: Building and Training Transformer Models for the Igbo LanguageCode0
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-IdentificationCode0
Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate DetectionCode0
Domain-independent Dominance of Adaptive MethodsCode0
IIT (BHU) Varanasi at MSR-SRST 2018: A Language Model Based Approach for Natural Language GenerationCode0
Automating the Correctness Assessment of AI-generated Code for Security ContextsCode0
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary DistillationCode0
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated ResponsesCode0
Domain Private Transformers for Multi-Domain Dialog SystemsCode0
Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language ModelsCode0
AutoML-guided Fusion of Entity and LLM-based Representations for Document ClassificationCode0
Domain Specific Author Attribution Based on Feedforward Neural Network Language ModelsCode0
MonoCoder: Domain-Specific Code Language Model for HPC Codes and TasksCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formulaCode0
Circuit Stability Characterizes Language Model GeneralizationCode0
Domain-Specific Language Model Post-Training for Indonesian Financial NLPCode0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Image-guided topic modeling for interpretable privacy classificationCode0
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content CounterfactuallyCode0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language TasksCode0
CitePrompt: Using Prompts to Identify Citation Intent in Scientific PapersCode0
IMHO Fine-Tuning Improves Claim DetectionCode0
CItruS: Chunked Instruction-aware State Eviction for Long Sequence ModelingCode0
Active Inference for Self-Organizing Multi-LLM Systems: A Bayesian Thermodynamic Approach to AdaptationCode0
Impact of representation matching with neural machine translationCode0
Impact of SMILES Notational Inconsistencies on Chemical Language Model PerformanceCode0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
A Linguistic Comparison between Human and ChatGPT-Generated ConversationsCode0
Do Pre-trained Vision-Language Models Encode Object States?Code0
Implicit Deep Latent Variable Models for Text GenerationCode0
Implicit Language Model in LSTM for OCRCode0
Implicit N-grams Induced by RecurrenceCode0
Do RNNs learn human-like abstract word order preferences?Code0
Claim Optimization in Computational ArgumentationCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Importance Weighting Can Help Large Language Models Self-ImproveCode0
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?Code0
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial NetworksCode0
CLAIR-A: Leveraging Large Language Models to Judge Audio CaptionsCode0
Do Transformers Need Deep Long-Range MemoryCode0
Show:102550
← PrevPage 103 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