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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13451–13500 of 17610 papers

TitleStatusHype
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models—0
FG-MDM: Towards Zero-Shot Human Motion Generation via ChatGPT-Refined Descriptions—0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Generating Image Descriptions using Multilingual Data—0
Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data—0
Generating long-horizon stock "buy" signals with a neural language model—0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention—0
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge—0
Generating Paraphrases from DBPedia using Deep Learning—0
Entity-Centric Query Refinement—0
Generating Sequences by Learning to Self-Correct—0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation—0
Generation, Distillation and Evaluation of Motivational Interviewing-Style Reflections with a Foundational Language Model—0
Generation from Abstract Meaning Representation using Tree Transducers—0
Generation of Hip-Hop Lyrics with Hierarchical Modeling and Conditional Templates—0
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Generative Adversarial Training Can Improve Neural Language Models—0
Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence—0
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision—0
Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model—0
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models—0
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023—0
Generative AI for Visualization: State of the Art and Future Directions—0
Generative AI in the Construction Industry: A State-of-the-art Analysis—0
Generative AI Text Classification using Ensemble LLM Approaches—0
Generative Artificial Intelligence-Guided User Studies: An Application for Air Taxi Services—0
Generative Bridging Network for Neural Sequence Prediction—0
Generative Bridging Network in Neural Sequence Prediction—0
Generative Design through Quality-Diversity Data Synthesis and Language Models—0
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Generative error correction for code-switching speech recognition using large language models—0
Generative Humanization for Therapeutic Antibodies—0
Generative Incremental Dependency Parsing with Neural Networks—0
Generative Job Recommendations with Large Language Model—0
Generative Knowledge Transfer for Neural Language Models—0
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule—0
Generative Language Model for Catalyst Discovery—0
Generative Language Modeling for Automated Theorem Proving—0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
Generative Linguistics, Large Language Models, and the Social Nature of Scientific Success—0
Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?—0
Generative Organizational Behavior Simulation using Large Language Model based Autonomous Agents: A Holacracy Perspective—0
Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering—0
Generative Pre-Trained Transformers for Biologically Inspired Design—0
Generative Prompt Tuning for Relation Classification—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