SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13901–13950 of 17610 papers

TitleStatusHype
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses—0
Tracing the Genealogies of Ideas with Large Language Model Embeddings—0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
TrackVLA: Embodied Visual Tracking in the Wild—0
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases—0
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System—0
Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space—0
Training a code-switching language model with monolingual data—0
Training and Analysing Deep Recurrent Neural Networks—0
Training a T5 Using Lab-sized Resources—0
Training a Tokenizer for Free with Private Federated Learning—0
Training a Vision Language Model as Smartphone Assistant—0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Training Data for Large Language Model—0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data—0
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments—0
Training-Free Mitigation of Language Reasoning Degradation After Multimodal Instruction Tuning—0
Training-Free Query Optimization via LLM-Based Plan Similarity—0
Training-Free Semantic Segmentation via LLM-Supervision—0
Training Hybrid Language Models by Marginalizing over Segmentations—0
Training Large Language Models Efficiently with Sparsity and Dataflow—0
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices—0
Training Linear Finite-State Machines—0
Training microrobots to swim by a large language model—0
Training Multilingual Pre-trained Language Model with Byte-level Subwords—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model—0
Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems—0
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
Training self-supervised peptide sequence models on artificially chopped proteins—0
Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer—0
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers—0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—0
Transcending Scaling Laws with 0.1% Extra Compute—0
TransDiffuser: End-to-end Trajectory Generation with Decorrelated Multi-modal Representation for Autonomous Driving—0
Trans-dimensional Random Fields for Language Modeling—0
Transducer Disambiguation with Sparse Topological Features—0
Transferable speech-to-text large language model alignment module—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary—0
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources—0
Transfer Learning for British Sign Language Modelling—0
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization—0
Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022—0
Transfer learning of language-independent end-to-end ASR with language model fusion—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—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