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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7801–7850 of 17610 papers

TitleStatusHype
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
Thank ``Goodness''! A Way to Measure Style in Student Essays—0
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media—0
The Acceptability Delta Criterion: Testing Knowledge of Language using the Gradience of Sentence Acceptability—0
The AFRL IWSLT 2020 Systems: Work-From-Home Edition—0
The AFRL-MITLL WMT15 System: There's More than One Way to Decode It!—0
The AFRL-MITLL WMT16 News-Translation Task Systems—0
The Amazing World of Neural Language Generation—0
The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation—0
The Anatomy of a Search and Mining System for Digital Archives—0
TheanoLM - An Extensible Toolkit for Neural Network Language Modeling—0
The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale—0
The Benefit of Syntactic vs. Linear N-grams for Linguistic Description—0
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada—0
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset—0
The Big Send-off: High Performance Collectives on GPU-based Supercomputers—0
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives—0
The boundaries of meaning: a case study in neural machine translation—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