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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6401–6450 of 17610 papers

TitleStatusHype
Augmenting emotion features in irony detection with Large language modeling—0
Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment—0
Augmenting Language Models with Long-Term Memory—0
Augmenting Large Language Model Translators via Translation Memories—0
Augmenting LLMs with Knowledge: A survey on hallucination prevention—0
Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation—0
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics—0
A Unified Framework for Grammar Error Correction—0
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks—0
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units—0
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System—0
An AI-driven multimodal smart home platform for continuous monitoring and intelligent assistance in post-stroke patients—0
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content—0
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code—0
Autellix: An Efficient Serving Engine for LLM Agents as General Programs—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
AutoAD II: The Sequel - Who, When, and What in Movie Audio Description—0
AutoAD II: The Sequel -- Who, When, and What in Movie Audio Description—0
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models—0
Autocorrect for Estonian texts: final report from project EKTB25—0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation—0
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA—0
AutoFPDesigner: Automated Flight Procedure Design Based on Multi-Agent Large Language Model—0
Auto-GNN: Neural Architecture Search of Graph Neural Networks—0
AutoGraphex: Zero-shot Biomedical Definition Generation with Automatic Prompting—0
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents—0
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents—0
EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance—0
Automata-based constraints for language model decoding—0
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation—0
Automated Data Curation for Robust Language Model Fine-Tuning—0
Automated detection of atomicity violations in large-scale systems—0
Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems—0
Automated Extraction of Acronym-Expansion Pairs from Scientific Papers—0
Automated Genre-Aware Article Scoring and Feedback Using Large Language Models—0
Automated Journalistic Questions: A New Method for Extracting 5W1H in French—0
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation—0
Automated Question Generation on Tabular Data for Conversational Data Exploration—0
Automated radiotherapy treatment planning guided by GPT-4Vision—0
Automated Reading Passage Generation with OpenAI's Large Language Model—0
Automated Real-World Sustainability Data Generation from Images of Buildings—0
Automated Repair of AI Code with Large Language Models and Formal Verification—0
Automated Root Cause Analysis System for Complex Data Products—0
Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling—0
Automated speech-unit delimitation in spoken learner English—0
Automated Statistical Model Discovery with Language Models—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