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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11851–11900 of 17610 papers

TitleStatusHype
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval—0
Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation—0
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation—0
Visual Features for Context-Aware Speech Recognition—0
Using Collocations and K-means Clustering to Improve the N-pos Model for Japanese IME—0
Using cognitive psychology to understand GPT-3—0
Visual grounding for desktop graphical user interfaces—0
Visual Grounding Strategies for Text-Only Natural Language Processing—0
Visualizing and Explaining Language Models—0
Visualizing and Understanding the Effectiveness of BERT—0
Visualizing Attention in Transformer-Based Language Representation Models—0
Visualizing attention zones in machine reading comprehension models—0
Visualizing Linguistic Shift—0
Visualizing RNN States with Predictive Semantic Encodings—0
Visualizing the Content of a Children's Story in a Virtual World: Lessons Learned—0
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance—0
Visual Language Modeling on CNN Image Representations—0
Visual-Language Model Knowledge Distillation Method for Image Quality Assessment—0
Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
You should evaluate your language model on marginal likelihood over tokenisations—0
"You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations—0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL—0
Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination—0
Using ASR-Generated Text for Spoken Language Modeling—0
Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses—0
Semantically-Prompted Language Models Improve Visual Descriptions—0
Visually Grounded Language Learning: a review of language games, datasets, tasks, and models—0
Like a Baby: Visually Situated Neural Language Acquisition—0
Using an LLM to Turn Sign Spottings into Spoken Language Sentences—0
VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning—0
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models—0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education—0
Using a Large Language Model to generate a Design Structure Matrix—0
Controllable Speaking Styles Using a Large Language Model—0
Visual Speech Language Models—0
Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Visual Text Generation in the Wild—0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems—0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches—0
ViTOC: Vision Transformer and Object-aware Captioner—0
VITRO: Vocabulary Inversion for Time-series Representation Optimization—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study—0
VL-BEiT: Generative Vision-Language Pretraining—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