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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 13501–13550 of 17610 papers

TitleStatusHype
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of ColorCode0
Kronecker Decomposition for GPT Compression—0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue—0
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models—0
MIMICause: Representation and automatic extraction of causal relation types from clinical notes—0
Spoken ObjectNet: A Bias-Controlled Spoken Caption DatasetCode0
Sparks: Inspiration for Science Writing using Language Models—0
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models—0
bert2BERT: Towards Reusable Pretrained Language Models—0
Dict-BERT: Enhancing Language Model Pre-training with DictionaryCode0
On Language Model Integration for RNN Transducer based Speech Recognition—0
Maximizing Efficiency of Language Model Pre-training for Learning Representation—0
Language Modelling via Learning to Rank—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
Multi-Modal Pre-Training for Automated Speech Recognition—0
Deep Learning for Bias Detection: From Inception to Deployment—0
Balancing Average and Worst-case Accuracy in Multitask Learning—0
Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling—0
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy—0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Unsupervised Neural Machine Translation with Generative Language Models Only—0
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition—0
Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems—0
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias—0
Automatic Text Extractive Summarization Based on Graph and Pre-trained Language Model Attention—0
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms—0
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models—0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition—0
Beam Search with Bidirectional Strategies for Neural Response Generation—0
Back from the future: bidirectional CTC decoding using future information in speech recognition—0
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition—0
Cut the CARP: Fishing for zero-shot story evaluation—0
ABC: Attention with Bounded-memory Control—0
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style TransferCode0
Objects in Semantic Topology—0
Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models—0
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
ASR Rescoring and Confidence Estimation with ELECTRA—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
A Survey On Neural Word Embeddings—0
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts—0
Contextualized Semantic Distance between Highly Overlapped TextsCode0
Leveraging Information Bottleneck for Scientific Document Summarization—0
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning—0
Show:102550
← PrevPage 271 of 353Next →
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