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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13951–14000 of 17610 papers

TitleStatusHype
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya—0
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching—0
Transferring Representations of Logical Connectives—0
Transfer training from smaller language model—0
Transformer-based Acoustic Modeling for Hybrid Speech Recognition—0
Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Transformer-based Causal Language Models Perform Clustering—0
Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress—0
Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection—0
Transformer-based language modeling and decoding for conversational speech recognition—0
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts—0
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens—0
Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)—0
Transformer-based Single-Cell Language Model: A Survey—0
TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication—0
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale—0
Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games—0
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs—0
Transformer protein language models are unsupervised structure learners—0
Transformer-QL: A Step Towards Making Transformer Network Quadratically Large—0
Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19—0
Transformers and Large Language Models for Chemistry and Drug Discovery—0
Transformers Are Universally Consistent—0
Transformers are Universal Predictors—0
Transformers in Reinforcement Learning: A Survey—0
Transformers learn variable-order Markov chains in-context—0
Transformers meet Neural Algorithmic Reasoners—0
Transformer with Bidirectional Decoder for Speech Recognition—0
Transformer with Fourier Integral Attentions—0
Transformer-XL: Language Modeling with Longer-Term Dependency—0
Transforming and Combining Rewards for Aligning Large Language Models—0
Transforming Calabi-Yau Constructions: Generating New Calabi-Yau Manifolds with Transformers—0
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication—0
Transforming Wearable Data into Health Insights using Large Language Model Agents—0
TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling—0
TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation—0
Transitional Adaptation of Pretrained Models for Visual Storytelling—0
Transition-based Knowledge Graph Embedding with Relational Mapping Properties—0
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
Translating a Language You Don't Know In the Chinese Room—0
Translating away Translationese without Parallel Data—0
Translating Chinese Unknown Words by Automatically Acquired Templates—0
Translating Collocation using Monolingual and Parallel Corpus—0
Translating Natural Language Queries to SQL Using the T5 Model—0
Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential—0
Translation Assistance by Translation of L1 Fragments in an L2 Context—0
Translation Memory Guided Neural Machine Translation—0
Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information—0
Show:102550
← PrevPage 280 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