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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9451–9500 of 17610 papers

TitleStatusHype
Unified Attacks to Large Language Model Watermarks: Spoofing and Scrubbing in Unauthorized Knowledge Distillation—0
Why Gradients Rapidly Increase Near the End of Training—0
Vision Language Transformers: A Survey—0
VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection—0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks—0
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI—0
University of Copenhagen Participation in TREC Health Misinformation Track 2020—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
University of Rochester WMT 2017 NMT System Submission—0
Word surprisal predicts N400 amplitude during reading—0
A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction—0
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation—0
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving—0
Why LLMs Cannot Think and How to Fix It—0
Visual attention models for scene text recognition—0
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling—0
Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation—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
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval—0
Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation—0
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation—0
Visual Features for Context-Aware Speech Recognition—0
Why Neural Translations are the Right Length—0
Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling—0
Visual grounding for desktop graphical user interfaces—0
Visual Grounding Strategies for Text-Only Natural Language Processing—0
Visualizing and Explaining Language Models—0
Visualizing and Understanding the Effectiveness of BERT—0
Visualizing Attention in Transformer-Based Language Representation Models—0
Visualizing attention zones in machine reading comprehension models—0
Visualizing Linguistic Shift—0
Visualizing RNN States with Predictive Semantic Encodings—0
Visualizing the Content of a Children's Story in a Virtual World: Lessons Learned—0
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance—0
Visual Language Modeling on CNN Image Representations—0
Visual-Language Model Knowledge Distillation Method for Image Quality Assessment—0
Unsupervised Aspect-Level Sentiment Controllable Style Transfer—0
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet—0
Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks—0
Why Would You Suggest That? Human Trust in Language Model Responses—0
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization—0
Unsupervised ASR via Cross-Lingual Pseudo-Labeling—0
Wiki-40B: Multilingual Language Model Dataset—0
XD: Cross-lingual Knowledge Distillation for Polyglot Sentence Embeddings—0
Semantically-Prompted Language Models Improve Visual Descriptions—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