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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8751–8800 of 17610 papers

TitleStatusHype
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
English to Chinese Translation: How Chinese Character Matters—0
English to Indonesian Transliteration to Support English Pronunciation Practice—0
Enhance audio generation controllability through representation similarity regularization—0
Enhanced Classroom Dialogue Sequences Analysis with a Hybrid AI Agent: Merging Expert Rule-Base with Large Language Models—0
Enhanced Computationally Efficient Long LoRA Inspired Perceiver Architectures for Auto-Regressive Language Modeling—0
Enhanced Facet Generation with LLM Editing—0
Enhanced Modality Transition for Image Captioning—0
Enhanced User Interaction in Operating Systems through Machine Learning Language Models—0
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation—0
Enhance Reasoning Ability of Visual-Language Models via Large Language Models—0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO—0
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets—0
Enhancing Annotated Bibliography Generation with LLM Ensembles—0
Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework—0
Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models—0
Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models—0
Enhancing Attention with Explicit Phrasal Alignments—0
Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics—0
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
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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