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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 13401–13450 of 17610 papers

TitleStatusHype
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling—0
Test-Time Adaptation for Visual Document Understanding—0
Test Time Learning for Time Series Forecasting—0
Test-Time Training Provably Improves Transformers as In-context Learners—0
t-Exponential Memory Networks for Question-Answering Machines—0
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model—0
Text-aware and Context-aware Expressive Audiobook Speech Synthesis—0
Text-Based Action-Model Acquisition for Planning—0
Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging—0
Text-based Person Search without Parallel Image-Text Data—0
TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model—0
Textbooks Are All You Need—0
Text Classification of Cancer Clinical Trial Eligibility Criteria—0
Text Clustering with Large Language Model Embeddings—0
Text Completion using Context-Integrated Dependency Parsing—0
Text Compression for Efficient Language Generation—0
Alt-Text with Context: Improving Accessibility for Images on Twitter—0
Text-Conditioned Resampler For Long Form Video Understanding—0
Text Data Augmentation: Towards better detection of spear-phishing emails—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS—0
Text Enriched Sparse Hyperbolic Graph Convolutional Networks—0
Text-Free Prosody-Aware Generative Spoken Language Modeling—0
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model—0
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models—0
Labor Space: A Unifying Representation of the Labor Market via Large Language Models—0
TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text—0
Textless Streaming Speech-to-Speech Translation using Semantic Speech Tokens—0
Memory^3: Language Modeling with Explicit Memory—0
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets—0
Text Normalization Infrastructure that Scales to Hundreds of Language Varieties—0
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer—0
Image Captioning with Multi-Context Synthetic Data—0
Text-only Synthesis for Image Captioning—0
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design—0
Text Rendering Strategies for Pixel Language Models—0
TextRGNN: Residual Graph Neural Networks for Text Classification—0
Text Segmentation by Language Using Minimum Description Length—0
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks—0
Text Style Transfer for Bias Mitigation using Masked Language Modeling—0
Text Summarization with Latent Queries—0
Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation—0
Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval—0
Text-to-Code Generation with Modality-relative Pre-training—0
Text-to-Table: A New Way of Information Extraction—0
Textual Data Augmentation for Patient Outcomes Prediction—0
Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations—0
TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings—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