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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 14651–14700 of 17610 papers

TitleStatusHype
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
GM-RKB WikiText Error Correction Task and BaselinesCode0
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature—0
Is Language Modeling Enough? Evaluating Effective Embedding Combinations—0
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence InsertionCode1
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization—0
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru—0
Stylometry in a Bilingual Setup—0
Wiki-40B: Multilingual Language Model Dataset—0
Improving the Language Model for Low-Resource ASR with Online Text Corpora—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020—0
Aggression Identification in Social Media: a Transfer Learning Based Approach—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—0
Automatic Myanmar Image Captioning using CNN and LSTM-Based Language Model—0
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur—0
Offensive language detection in Arabic using ULMFiTCode1
On the Exploration of English to Urdu Machine Translation—0
Neural Models for Predicting Celtic Mutations—0
Speech Transcription Challenges for Resource Constrained Indigenous Language Cree—0
The INCOMSLAV Platform: Experimental Website with Integrated Methods for Measuring Linguistic Distances and Asymmetries in Receptive Multilingualism—0
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition—0
IRIT at TRAC 2020—0
Multi-Task Learning using AraBert for Offensive Language Detection—0
Language Models for Cloze Task Answer Generation in Russian—0
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-trainingCode1
Multi-scale Transformer Language Models—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition—0
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-trainingCode1
AdapterFusion: Non-Destructive Task Composition for Transfer LearningCode2
Style Variation as a Vantage Point for Code-Switching—0
Selecting Informative Contexts Improves Language Model Finetuning—0
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause AttachmentCode0
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERTCode0
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Language Model Prior for Low-Resource Neural Machine TranslationCode1
Modelling Suspense in Short Stories as Uncertainty Reduction over Neural RepresentationCode1
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language ModelsCode1
Knowledge Injection into Dialogue Generation via Language Models—0
Context based Text-generation using LSTM networks—0
An Empirical Study of Pre-trained Transformers for Arabic Information ExtractionCode1
Few-Shot Learning for Opinion SummarizationCode1
APo-VAE: Text Generation in Hyperbolic Space—0
Aspect-Controlled Neural Argument GenerationCode1
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation—0
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks—0
AMPERSAND: Argument Mining for PERSuAsive oNline DiscussionsCode1
Template Guided Text Generation for Task-Oriented Dialogue—0
Show:102550
← PrevPage 294 of 353Next →
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