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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1610116150 of 17610 papers

TitleStatusHype
ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented RealityCode0
VideoBERT: A Joint Model for Video and Language Representation LearningCode0
Video (language) modeling: a baseline for generative models of natural videosCode0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
VILA: Learning Image Aesthetics from User Comments with Vision-Language PretrainingCode0
Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language ModelsCode0
ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action RecognitionCode0
ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding ValidationCode0
Virology Capabilities Test (VCT): A Multimodal Virology Q&A BenchmarkCode0
VirusT5: Harnessing Large Language Models to Predicting SARS-CoV-2 EvolutionCode0
Vision Conformer: Incorporating Convolutions into Vision Transformer LayersCode0
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized ModelsCode0
Vision-Language In-Context Learning Driven Few-Shot Visual Inspection ModelCode0
Vision-Language Pre-Training for Boosting Scene Text DetectorsCode0
VisionThink: Smart and Efficient Vision Language Model via Reinforcement LearningCode0
ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text ProcessingCode0
VIS-Shepherd: Constructing Critic for LLM-based Data Visualization GenerationCode0
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language ModelCode0
Visually-Aware Context Modeling for News Image CaptioningCode0
Visually Dehallucinative Instruction GenerationCode0
Visually Dehallucinative Instruction Generation: Know What You Don't KnowCode0
Visual Re-ranking with Natural Language Understanding for Text SpottingCode0
VIXEN: Visual Text Comparison Network for Image Difference CaptioningCode0
VLTP: Vision-Language Guided Token Pruning for Task-Oriented SegmentationCode0
VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty EstimationCode0
Vocabulary-free Image Classification and Semantic SegmentationCode0
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic SegmentationCode0
VSCBench: Bridging the Gap in Vision-Language Model Safety CalibrationCode0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology ImagingCode0
V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLMCode0
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge GraphsCode0
Wanda++: Pruning Large Language Models via Regional GradientsCode0
WatChat: Explaining perplexing programs by debugging mental modelsCode0
Watch What You Just Said: Image Captioning with Text-Conditional AttentionCode0
Watermark under Fire: A Robustness Evaluation of LLM WatermarkingCode0
Scaling Capability in Token Space: An Analysis of Large Vision Language ModelCode0
We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responsesCode0
Web Page Classification using LLMs for Crawling SupportCode0
We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard TextCode0
WET: Overcoming Paraphrasing Vulnerabilities in Embeddings-as-a-Service with Linear Transformation WatermarksCode0
What a neural language model tells us about spatial relationsCode0
What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelsCode0
What Does BERT Look At? An Analysis of BERT's AttentionCode0
What Do Llamas Really Think? Revealing Preference Biases in Language Model RepresentationsCode0
What Do Recurrent Neural Network Grammars Learn About Syntax?Code0
What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structureCode0
What Makes Pre-trained Language Models Better Zero-shot Learners?Code0
What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD FilesCode0
What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token PatternsCode0
When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?Code0
Show:102550
← PrevPage 323 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