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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7051–7100 of 17610 papers

TitleStatusHype
Fast and Robust Neural Network Joint Models for Statistical Machine Translation—0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition—0
Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation—0
Fast Collocation-Based Bayesian HMM Word Alignment—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
Fast-ELECTRA for Efficient Pre-training—0
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT—0
Faster Adaptive Federated Learning—0
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems—0
Faster Phrase-Based Decoding by Refining Feature State—0
Fast Gated Neural Domain Adaptation: Language Model as a Case Study—0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network—0
FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire—0
Fast Parametric Learning with Activation Memorization—0
Fast Quantum Algorithm for Attention Computation—0
Fast-Slow Thinking for Large Vision-Language Model Reasoning—0
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining—0
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network—0
FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA—0
FA Team at the NTCIR-17 UFO Task—0
Fault Diagnosis in Power Grids with Large Language Model—0
FBK-UPV-UEdin participation in the WMT14 Quality Estimation shared-task—0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration—0
Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Feature Decay Algorithms for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Feature Engineering vs BERT on Twitter Data—0
Feature Extraction for Native Language Identification Using Language Modeling—0
Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images—0
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales—0
Feature Optimization for Predicting Readability of Arabic L1 and L2—0
FedBaF: Federated Learning Aggregation Biased by a Foundation Model—0
FedBoost: A Communication-Efficient Algorithm for Federated Learning—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Federated Evaluation of On-device Personalization—0
Integration of Large Language Models and Federated Learning—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Federated Learning of N-gram Language Models—0
Federated Learning for Personalized Humor Recognition—0
Federated Reinforcement Learning with Constraint Heterogeneity—0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion—0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning—0
FedTLU: Federated Learning with Targeted Layer Updates—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency—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