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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7351–7400 of 17610 papers

TitleStatusHype
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech—0
Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature—0
GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding—0
Game Agent Driven by Free-Form Text Command: Using LLM-based Code Generation and Behavior Branch—0
GameGPT: Multi-agent Collaborative Framework for Game Development—0
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts—0
Gated ConvNets for Letter-Based ASR—0
Gated Feedback Recurrent Neural Networks—0
Gated Recurrent Neural Tensor Network—0
GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections—0
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks—0
GEB-1.3B: Open Lightweight Large Language Model—0
GECKO: Generative Language Model for English, Code and Korean—0
GEM: Generative Enhanced Model for adversarial attacks—0
Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts—0
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing—0
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence—0
genCNN: A Convolutional Architecture for Word Sequence Prediction—0
genCNN: A Convolutional Architecture for Word Sequence Prediction—0
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr—0
Gender-Distinguishing Features in Film Dialogue—0
Gender mobility in the labor market with skills-based matching models—0
Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts—0
Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval—0
Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models—0
GenDistiller: Distilling Pre-trained Language Models based on Generative Models—0
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling—0
generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation—0
General Framework for Reversible Data Hiding in Texts Based on Masked Language Modeling—0
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning—0
Generalizable Entity Grounding via Assistance of Large Language Model—0
Generalization Bias in Large Language Model Summarization of Scientific Research—0
Generalization in Generation: A closer look at Exposure Bias—0
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model—0
Generalization Measures for Zero-Shot Cross-Lingual Transfer—0
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data—0
Generalized Multiple Intent Conditioned Slot Filling—0
Generalized Probabilistic Attention Mechanism in Transformers—0
Generalizing Large Language Model Usability Across Resource-Constrained—0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning—0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
General Point Model with Autoencoding and Autoregressive—0
General-purpose Clothes Manipulation with Semantic Keypoints—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—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