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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12601–12650 of 17610 papers

TitleStatusHype
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking—0
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation—0
Enhancing BERT-Based Visual Question Answering through Keyword-Driven Sentence Selection—0
Enhancing BERT for Lexical Normalization—0
Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy—0
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning—0
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification—0
Enhancing Clinical Concept Extraction with Contextual Embeddings—0
Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients—0
Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models—0
Enhancing Code-switching Speech Recognition with Interactive Language Biases—0
Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning—0
Enhancing Context Through Contrast—0
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation—0
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking—0
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Enhancing DNA Foundation Models to Address Masking Inefficiencies—0
Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions—0
Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them—0
Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting—0
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation—0
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling—0
Enhancing Gait Video Analysis in Neurodegenerative Diseases by Knowledge Augmentation in Vision Language Model—0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study—0
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning—0
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns—0
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy—0
Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models—0
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model—0
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning—0
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting—0
Enhancing Large Language Model-based Speech Recognition by Contextualization for Rare and Ambiguous Words—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals—0
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately—0
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction—0
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System—0
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)—0
Enhancing Medical Specialty Assignment to Patients using NLP Techniques—0
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies—0
Enhancing Medication Recommendation with LLM Text Representation—0
Enhancing Multi-Criteria Decision Analysis with AI: Integrating Analytic Hierarchy Process and GPT-4 for Automated Decision Support—0
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing—0
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion—0
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
Enhancing Pre-trained Language Model with Lexical Simplification—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