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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8701–8750 of 17610 papers

TitleStatusHype
Linguistic and Structural Basis of Engineering Design Knowledge—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance—0
Employing Phonetic Speech Recognition for Language and Dialect Specific Search—0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Empowering Language Models with Knowledge Graph Reasoning for Question Answering—0
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking—0
Language Model Empowered Spatio-Temporal Forecasting via Physics-Aware Reprogramming—0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Empowering Working Memory for Large Language Model Agents—0
Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models—0
Enabling Autoregressive Models to Fill In Masked Tokens—0
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud—0
Enabling Inclusive Systematic Reviews: Incorporating Preprint Articles with Large Language Model-Driven Evaluations—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis—0
Enabling Real-time Neural IME with Incremental Vocabulary Selection—0
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning—0
Enabling text readability awareness during the micro planning phase of NLG applications—0
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Encoding Source Language with Convolutional Neural Network for Machine Translation—0
End2End Acoustic to Semantic Transduction—0
End-to-end Adaptive Distributed Training on PaddlePaddle—0
End-to-End ASR-free Keyword Search from Speech—0
End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach—0
End-to-End Code-Switching ASR for Low-Resourced Language Pairs—0
End-to-end Concept Word Detection for Video Captioning, Retrieval, and Question Answering—0
End-to-end Joint Punctuated and Normalized ASR with a Limited Amount of Punctuated Training Data—0
End-to-End Multimodal Speech Recognition—0
End-to-end Planner Training for Language Modeling—0
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting—0
End-to-end Reference-free Single-document Summary Quality Assessment—0
End-to-End Speech Recognition Contextualization with Large Language Models—0
End-to-End Speech Recognition: A Survey—0
End-to-End Speech Recognition with Pre-trained Masked Language Model—0
End-to-end Task-oriented Dialog Policy Learning based on Pre-trained Language Model—0
end-to-end training of a large vocabulary end-to-end speech recognition system—0
Energy and Carbon Considerations of Fine-Tuning BERT—0
Energy-Based Diffusion Language Models for Text Generation—0
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement—0
Energy-Based Models with Applications to Speech and Language Processing—0
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas—0
Engineering A Large Language Model From Scratch—0
English Conversational Telephone Speech Recognition by Humans and Machines—0
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019—0
English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach—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