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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12501–12550 of 17610 papers

TitleStatusHype
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus—0
Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions—0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition—0
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning—0
Multi-Fidelity Methods for Optimization: A Survey—0
Multi-Grained Chinese Word Segmentation—0
Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition—0
Multi-Granularity Semantic Revision for Large Language Model Distillation—0
Multi-Graph Decoding for Code-Switching ASR—0
Multi-group Uncertainty Quantification for Long-form Text Generation—0
Multi-Head State Space Model for Speech Recognition—0
Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes—0
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model—0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition—0
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning—0
Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study—0
Multilingual and Multimodal Learning for Brazilian Portuguese—0
Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models—0
Multilingual BERT Post-Pretraining Alignment—0
Neural Approaches to Multilingual Information Retrieval—0
Multilingual Comparative Analysis of Deep-Learning Dependency Parsing Results Using Parallel Corpora—0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages—0
Multilingual Few-Shot Learning via Language Model Retrieval—0
Multilingual Language Model Pretraining using Machine-translated Data—0
Multilingual Language Models for Named Entity Recognition in German and English—0
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers—0
Multilingual prediction of Alzheimer's disease through domain adaptation and concept-based language modelling—0
Multilingual Pre-training with Universal Dependency Learning—0
Multilingual Reliability and ``Semantic'' Structure of Continuous Word Spaces—0
Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching—0
Multilingual Sequence Labeling Approach to solve Lexical Normalization—0
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling—0
Multilingual Slavic Named Entity Recognition—0
Multilingual Speech Recognition With A Single End-To-End Model—0
Multilingual Substitution-based Word Sense Induction—0
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion—0
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion—0
Multilingual Test Sets for Machine Translation of Search Queries for Cross-Lingual Information Retrieval in the Medical Domain—0
Multilingual Tourist Assistance using ChatGPT: Comparing Capabilities in Hindi, Telugu, and Kannada—0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages—0
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages—0
Multi-lingual Wikipedia Summarization and Title Generation On Low Resource Corpus—0
Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models—0
Multi-megabase scale genome interpretation with genetic language models—0
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage—0
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Multimodal Classification of Teaching Activities from University Lecture Recordings—0
Multi-modal clothing recommendation model based on large model and VAE enhancement—0
Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction—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