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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10201–10250 of 17610 papers

TitleStatusHype
PersonaFlow: Boosting Research Ideation with LLM-Simulated Expert Personas—0
Personal Intelligence System UniLM: Hybrid On-Device Small Language Model and Server-Based Large Language Model for Malay Nusantara—0
Personality Structured Interview for Large Language Model Simulation in Personality Research—0
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles—0
Preference Adaptive and Sequential Text-to-Image Generation—0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
Personalized neural language models for real-world query auto completion—0
Personalized Response Generation with Tensor Factorization—0
Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising—0
Personalized Speech recognition on mobile devices—0
Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring—0
'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion—0
Person Re-Identification with Vision and Language—0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning—0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition—0
PeVL: Pose-Enhanced Vision-Language Model for Fine-Grained Human Action Recognition—0
PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models—0
PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization—0
PharmAgents: Building a Virtual Pharma with Large Language Model Agents—0
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping—0
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle—0
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone—0
PhilHumans: Benchmarking Machine Learning for Personal Health—0
Philippine Language Resources: Applications, Issues, and Directions—0
Phi-Omni-ST: A multimodal language model for direct speech-to-speech translation—0
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check—0
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data—0
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition—0
Phoneme Level Language Models for Sequence Based Low Resource ASR—0
Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems—0
Phoneme Similarity Matrices to Improve Long Audio Alignment for Automatic Subtitling—0
Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis—0
Phonetic Normalization for Machine Translation of User Generated Content—0
PhonologyBench: Evaluating Phonological Skills of Large Language Models—0
Phonotactic Modeling of Extremely Low Resource Languages—0
PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents—0
PhotoBot: Reference-Guided Interactive Photography via Natural Language—0
Phrasal: A Toolkit for New Directions in Statistical Machine Translation—0
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR—0
Phrase-aware Unsupervised Constituency Parsing—0
Phrase-aware Unsupervised Constituency Parsing—0
Phrase-based Image Captioning—0
Phrase Based Language Model for Statistical Machine Translation: Empirical Study—0
Phrase Based Language Model For Statistical Machine Translation—0
Phrase-Based SMT for Finnish with More Data, Better Models and Alternative Alignment and Translation Tools—0
PHRASED: Phrase Dictionary Biasing for Speech Translation—0
Phraselette: A Poet's Procedural Palette—0
Phrase-Level Class based Language Model for Mandarin Smart Speaker Query Recognition—0
PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation—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