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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10201–10250 of 17610 papers

TitleStatusHype
Multi-Modal Data Augmentation for End-to-End ASR—0
Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model—0
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios—0
Multimodal Few-Shot Learning with Frozen Language Models—0
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond—0
Multi-Modal Generative Embedding Model—0
Multi-modality Latent Interaction Network for Visual Question Answering—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles—0
Multimodal Large Language Model for Visual Navigation—0
Multimodal large language model for wheat breeding: a new exploration of smart breeding—0
Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation—0
Multi-modal Learning for WebAssembly Reverse Engineering—0
Multimodal LLMs for health grounded in individual-specific data—0
Multimodal Markup Document Models for Graphic Design Completion—0
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model—0
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization—0
Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model—0
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department—0
Multi-Modal Pre-Training for Automated Speech Recognition—0
Multimodal Programming in Computer Science with Interactive Assistance Powered by Large Language Model—0
Multimodal Representation Loss Between Timed Text and Audio for Regularized Speech Separation—0
Multi-Modal Retrieval For Large Language Model Based Speech Recognition—0
Multimodal Search on Iconclass using Vision-Language Pre-Trained Models—0
Multimodal Shannon Game with Images—0
Multimodal Transformer for Comics Text-Cloze—0
Multi-Modal Video Dialog State Tracking in the Wild—0
Multimodal Web Navigation with Instruction-Finetuned Foundation Models—0
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model—0
Multi-objective Evolution of Heuristic Using Large Language Model—0
Multi-Perspective Semantic Information Retrieval in the Biomedical Domain—0
Multiple Abstraction Level Retrieve Augment Generation—0
Multiple Adjunction in Feature-Based Tree-Adjoining Grammar—0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Multiplicative Models for Recurrent Language Modeling—0
MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World—0
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting—0
Multi-Reward based Reinforcement Learning for Neural Machine Translation—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
Multiscale Self Attentive Convolutions for Vision and Language Modeling—0
Multiscale sequence modeling with a learned dictionary—0
Multi-scale Transformer Language Models—0
Multi-segment preserving sampling for deep manifold sampler—0
Multi-Sense Language Modelling—0
Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy—0
Multi-source Neural Automatic Post-Editing: FBK's participation in the WMT 2017 APE shared task—0
Multi-stage Large Language Model Correction for Speech Recognition—0
Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment—0
Multi-Stage Pre-training for Low-Resource Domain Adaptation—0
Multi-Stage Pre-Training for Math-Understanding: ^2(AL)BERT—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