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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10151–10200 of 17610 papers

TitleStatusHype
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain—0
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions—0
PATHS: A System for Accessing Cultural Heritage Collections—0
Patience Is The Key to Large Language Model Reasoning—0
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation—0
Patton: Language Model Pretraining on Text-Rich Networks—0
PauseSpeech: Natural Speech Synthesis via Pre-trained Language Model and Pause-based Prosody Modeling—0
PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model—0
Paying Alignment Tax with Contrastive Learning—0
Payload-Aware Intrusion Detection with CMAE and Large Language Models—0
PBNR: Prompt-based News Recommender System—0
PCFG Induction for Unsupervised Parsing and Language Modelling—0
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning—0
P/D-Serve: Serving Disaggregated Large Language Model at Scale—0
PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models—0
PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding—0
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers—0
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework—0
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion—0
PEER: A Collaborative Language Model—0
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments—0
PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning—0
Peerus Review: a tool for scientific experts finding—0
PEFT-MedAware: Large Language Model for Medical Awareness—0
PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models—0
Pegasus-v1 Technical Report—0
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design—0
PEL-BERT: A Joint Model for Protocol Entity Linking—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
PEPT: Expert Finding Meets Personalized Pre-training—0
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion—0
Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language—0
Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference—0
Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading—0
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction—0
Permissive Information-Flow Analysis for Large Language Models—0
WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain—0
Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data—0
Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation—0
PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts—0
PersianLLaMA: Towards Building First Persian Large Language Model—0
PersianMind: A Cross-Lingual Persian-English Large Language Model—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists—0
PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time—0
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars—0
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement—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