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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11701–11750 of 17610 papers

TitleStatusHype
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use—0
RoBERTweet: A BERT Language Model for Romanian Tweets—0
RoboGolf: Mastering Real-World Minigolf with a Reflective Multi-Modality Vision-Language Model—0
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics—0
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT—0
Robotic Programmer: Video Instructed Policy Code Generation for Robotic Manipulation—0
Robotic State Recognition with Image-to-Text Retrieval Task of Pre-Trained Vision-Language Model and Black-Box Optimization—0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Robust Authorship Verification with Transfer Learning—0
Robust Chinese Word Segmentation with Contextualized Word Representations—0
Robust Document Representations using Latent Topics and Metadata—0
Robust Few-Shot Vision-Language Model Adaptation—0
Robustifying Language Models with Test-Time Adaptation—0
Designing Robust Transformers using Robust Kernel Density Estimation—0
Robust Prompt Optimization for Large Language Models Against Distribution Shifts—0
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction—0
Robustness of the Random Language Model—0
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts—0
Robust parfda Statistical Machine Translation Results—0
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
Robust Preference Optimization through Reward Model Distillation—0
Robust Stochastic Graph Generator for Counterfactual Explanations—0
Robust Text Classification using Sub-Word Information in Input Word Representations.—0
Robust Transmission of Punctured Text with Large Language Model-based Recovery—0
Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation—0
Role of Bias Terms in Dot-Product Attention—0
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding—0
Rollenwechsel-English: a large-scale semantic role corpus—0
RoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and Offense—0
Romanization Encoding For Multilingual ASR—0
RomanLens: Latent Romanization and its role in Multilinguality in LLMs—0
ROMUL: Scale Adaptative Population Based Training—0
RoQLlama: A Lightweight Romanian Adapted Language Model—0
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models—0
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning—0
Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment—0
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications—0
Routers in Vision Mixture of Experts: An Empirical Study—0
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model—0
RTM results for Predicting Translation Performance—0
Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records—0
Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System—0
Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin—0
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models—0
Run LoRA Run: Faster and Lighter LoRA Implementations—0
RWKV-UI: UI Understanding with Enhanced Perception and Reasoning—0
R+X: Retrieval and Execution from Everyday Human Videos—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