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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11501–11550 of 17610 papers

TitleStatusHype
RegionGPT: Towards Region Understanding Vision Language Model—0
Regression with Large Language Models for Materials and Molecular Property Prediction—0
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning—0
Regularized Training of Nearest Neighbor Language Models—0
Regularizing activations in neural networks via distribution matching with the Wasserstein metric—0
Regularizing Text Categorization with Clusters of Words—0
Regular Patterns - Probably Approximately Correct Language Model—0
Rehearsal: Simulating Conflict to Teach Conflict Resolution—0
Reinforced Self-Training (ReST) for Language Modeling—0
Reinforcement Learning for Generative AI: A Survey—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Reinforcement Learning is all You Need—0
Reinforcement Pre-Training—0
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification—0
Reinforcing Thinking through Reasoning-Enhanced Reward Models—0
Relating Neural Text Degeneration to Exposure Bias—0
Relational Database Augmented Large Language Model—0
Relational Memory Augmented Language Models—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
Relationship of the language distance to English ability of a country—0
Relations Prediction for Knowledge Graph Completion using Large Language Models—0
RelationVLM: Making Large Vision-Language Models Understand Visual Relations—0
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection—0
Relative Overfitting and Accept-Reject Framework—0
Relaxed Attention for Transformer Models—0
Relaxed Softmax for learning from Positive and Unlabeled data—0
Release Strategies and the Social Impacts of Language Models—0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
Relevance-Promoting Language Model for Short-Text Conversation—0
Rel-grams: A Probabilistic Model of Relations in Text—0
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge—0
Reliable Semantic Understanding for Real World Zero-shot Object Goal Navigation—0
RELIC: Investigating Large Language Model Responses using Self-Consistency—0
Remember what you did so you know what to do next—0
Reminding Multimodal Large Language Models of Object-aware Knowledge with Retrieved Tags—0
Reminding the Incremental Language Model via Data-Free Self-Distillation—0
Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model—0
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment—0
Remote Timing Attacks on Efficient Language Model Inference—0
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment—0
Repairing Bugs in Python Assignments Using Large Language Models—0
Repair Is Nearly Generation: Multilingual Program Repair with LLMs—0
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability—0
Repetition Facilitates Processing: The Processing Advantage of Construction Repetition in Dialogue—0
Rephrasing Electronic Health Records for Pretraining Clinical Language Models—0
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training—0
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling—0
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training—0
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents—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