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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12501–12550 of 17610 papers

TitleStatusHype
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought—0
Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students—0
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge—0
Embracing Large Language Models in Traffic Flow Forecasting—0
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations—0
Emergence of order in random languages—0
Emergent Abilities of Large Language Models—0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
Emergent inabilities? Inverse scaling over the course of pretraining—0
Emerging Cross-lingual Structure in Pretrained Language Models—0
Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations—0
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications—0
Emerging Property of Masked Token for Effective Pre-training—0
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models—0
emLam -- a Hungarian Language Modeling baseline—0
EMNLP@CPH: Is frequency all there is to simplicity?—0
EmoEdit: Evoking Emotions through Image Manipulation—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning—0
Emotion-based Modeling of Mental Disorders on Social Media—0
EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis—0
EmoUS: Simulating User Emotions in Task-Oriented Dialogues—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments—0
Emphasizing Unseen Words: New Vocabulary Acquisition for End-to-End Speech Recognition—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
Linguistic and Structural Basis of Engineering Design Knowledge—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance—0
Employing Phonetic Speech Recognition for Language and Dialect Specific Search—0
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities—0
Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Empowering Language Models with Knowledge Graph Reasoning for Question Answering—0
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking—0
Language Model Empowered Spatio-Temporal Forecasting via Physics-Aware Reprogramming—0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Empowering Working Memory for Large Language Model Agents—0
Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models—0
Enabling Autoregressive Models to Fill In Masked Tokens—0
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud—0
Enabling Inclusive Systematic Reviews: Incorporating Preprint Articles with Large Language Model-Driven Evaluations—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis—0
Enabling Real-time Neural IME with Incremental Vocabulary Selection—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