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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12551–12600 of 17610 papers

TitleStatusHype
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
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
English to Chinese Translation: How Chinese Character Matters—0
English to Indonesian Transliteration to Support English Pronunciation Practice—0
Enhance audio generation controllability through representation similarity regularization—0
Enhanced Classroom Dialogue Sequences Analysis with a Hybrid AI Agent: Merging Expert Rule-Base with Large Language Models—0
Enhanced Computationally Efficient Long LoRA Inspired Perceiver Architectures for Auto-Regressive Language Modeling—0
Enhanced Facet Generation with LLM Editing—0
Enhanced Modality Transition for Image Captioning—0
Enhanced User Interaction in Operating Systems through Machine Learning Language Models—0
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation—0
Enhance Reasoning Ability of Visual-Language Models via Large Language Models—0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO—0
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets—0
Enhancing Annotated Bibliography Generation with LLM Ensembles—0
Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework—0
Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models—0
Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models—0
Enhancing Attention with Explicit Phrasal Alignments—0
Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics—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