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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 55015550 of 17610 papers

TitleStatusHype
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationCode0
VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domainCode0
ValCAT: Generating Variable-Length Contextualized Adversarial Transformations using Encoder-DecoderCode0
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language ModelCode0
Valid Text-to-SQL Generation with Unification-based DeepStochLogCode0
Value-aware Approximate AttentionCode0
Value-Free Policy Optimization via Reward PartitioningCode0
Vamos: Versatile Action Models for Video UnderstandingCode0
VarBench: Robust Language Model Benchmarking Through Dynamic Variable PerturbationCode0
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-LevelsCode0
Variance Control via Weight Rescaling in LLM Pre-trainingCode0
Variance-reduced Clipping for Non-convex OptimizationCode0
Variational Autoencoders for Collaborative FilteringCode0
Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2Code0
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on TwitterCode0
Verbalized Representation Learning for Interpretable Few-Shot GeneralizationCode0
Verdi: Quality Estimation and Error Detection for Bilingual CorporaCode0
VerifiAgent: a Unified Verification Agent in Language Model ReasoningCode0
VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative AdversaryCode0
Very Large Language Model as a Unified Methodology of Text MiningCode0
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is OffensiveCode0
ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in VietnameseCode0
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
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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