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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10801–10850 of 17610 papers

TitleStatusHype
Recovering Missing Characters in Old Hawaiian Writing—0
Rectified Sparse Attention—0
Recurrent Attention Unit—0
Recurrent Continuous Translation Models—0
Recurrent Hierarchical Topic-Guided Neural Language Models—0
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning—0
遞迴式類神經網路語言模型應用額外資訊於語音辨識之研究 (Recurrent Neural Network-based Language Modeling with Extra Information Cues for Speech Recognition) [In Chinese]—0
Recurrent Neural Network Based Loanwords Identification in Uyghur—0
Recurrent Neural Network based Translation Quality Estimation—0
Recurrent Neural Network-based Tuple Sequence Model for Machine Translation—0
Recurrent Neural Network Language Model Adaptation Derived Document Vector—0
Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey—0
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview—0
Recurrent Neural Network with Word Embedding for Complaint Classification—0
Recursive Introspection: Teaching Language Model Agents How to Self-Improve—0
Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data—0
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild—0
Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement—0
RecycleGPT: An Autoregressive Language Model with Recyclable Module—0
RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises—0
RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools—0
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis—0
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation—0
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning—0
Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games—0
Red Teaming Large Language Models for Healthcare—0
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation—0
Reducing Exposure Bias in Training Recurrent Neural Network Transducers—0
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval—0
Reducing infrequent-token perplexity via variational corpora—0
Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification—0
Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy—0
Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation—0
Reducing Privacy Risks in Online Self-Disclosures with Language Models—0
Reducing Reasoning Costs: The Path of Optimization for Chain of Thought via Sparse Attention Mechanism—0
Reducing Retraining by Recycling Parameter-Efficient Prompts—0
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation—0
Reducing the Need for Backpropagation and Discovering Better Optima With Explicit Optimizations of Neural Networks—0
Redundancy Detection in ESL Writings—0
Re-embedding words—0
Refactoring Programs Using Large Language Models with Few-Shot Examples—0
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models—0
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
RefineCap: Concept-Aware Refinement for Image Captioning—0
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units—0
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning—0
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences—0
Show:102550
← PrevPage 217 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