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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 11801–11850 of 17610 papers

TitleStatusHype
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings—0
Using idiolects and sociolects to improve word prediction—0
Virtual Scientific Companion for Synchrotron Beamlines: A Prototype—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
Using Hypothesis Selection Based Features for Confusion Network MT System Combination—0
Vision and Intention Boost Large Language Model in Long-Term Action Anticipation—0
Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Tasks—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Vision-centric Token Compression in Large Language Model—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework—0
Vision-Integrated LLMs for Autonomous Driving Assistance : Human Performance Comparison and Trust Evaluation—0
Vision-Language Adaptive Mutual Decoder for OOV-STR—0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
Vision-language Assisted Attribute Learning—0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction—0
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection—0
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring—0
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces—0
Using Gender- and Polarity-Informed Models to Investigate Bias—0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives—0
Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment—0
Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft—0
Vision-Language Model IP Protection via Prompt-based Learning—0
Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime—0
UMDFood: Vision-language models boost food composition compilation—0
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors—0
Using Factored Word Representation in Neural Network Language Models—0
Vision Language Transformers: A Survey—0
VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection—0
Using Entropy Estimates for DAG-Based Ontologies—0
Using Domain-specific and Collaborative Resources for Term Translation—0
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI—0
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training—0
Vision Transformer Based Model for Describing a Set of Images as a Story—0
VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions—0
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One—0
Using Deep Object Features for Image Descriptions—0
A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction—0
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
Visual attention models for scene text recognition—0
Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model—0
Using Context Vectors in Improving a Machine Translation System with Bridge Language—0
Visual Captioning at Will: Describing Images and Videos Guided by a Few Stylized Sentences—0
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning—0
Visual Comparison of Language Model Adaptation—0
Visual Conceptual Blending with Large-scale Language and Vision Models—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified