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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7651–7700 of 17610 papers

TitleStatusHype
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training—0
Hallucination of speech recognition errors with sequence to sequence learning—0
Hallucinations are inevitable but can be made statistically negligible. The "innate" inevitability of hallucinations cannot explain practical LLM issues—0
Handle-based Mesh Deformation Guided By Vision Language Model—0
Hands-Free VR—0
Handwritten Text Recognition Results on the Bentham Collection with Improved Classical N-Gram-HMM methods—0
Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit—0
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
Hardness Masking via Auto-Regressive Language Model—0
HardTests: Synthesizing High-Quality Test Cases for LLM Coding—0
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM—0
Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge—0
HARE: HumAn pRiors, a key to small language model Efficiency—0
HarmonicEval: Multi-modal, Multi-task, Multi-criteria Automatic Evaluation Using a Vision Language Model—0
Harnessing Business and Media Insights with Large Language Models—0
Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems—0
Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support—0
Harnessing large-language models to generate private synthetic text—0
Harnessing Large Vision and Language Models in Agriculture: A Review—0
Harnessing the Plug-and-Play Controller by Prompting—0
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model—0
Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition—0
Harvesting Parallel Text in Multiple Languages with Limited Supervision—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Hash Layers For Large Sparse Models—0
Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation—0
Has My System Prompt Been Used? Large Language Model Prompt Membership Inference—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing—0
HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments—0
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition—0
Head-Lexicalized Bidirectional Tree LSTMs—0
Healing Powers of BERT: How Task-Specific Fine-Tuning Recovers Corrupted Language Models—0
Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model—0
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System—0
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing—0
Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition—0
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models—0
Hebbian learning the local structure of language—0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition—0
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation—0
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative—0
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation—0
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine—0
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish—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