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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1095111000 of 17610 papers

TitleStatusHype
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder0
Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation0
HanTrans: An Empirical Study on Cross-Era Transferability of Chinese Pre-trained Language ModelCode0
A Quantitative Analysis of Comparison of Emoji Sentiment: Taiwan Mandarin Users and English Users0
NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach0
Language Model Based Chinese Handwriting Address Recognition0
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language InstructionsCode0
Text-Only Training for Image Captioning using Noise-Injected CLIPCode2
Two-stage LLM Fine-tuning with Less Specialization and More Generalization0
Machine learning can guide experimental approaches for protein digestibility estimations0
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation0
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5Code1
The future is different: Large pre-trained language models fail in prediction tasks0
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding0
Training Vision-Language Models with Less Bimodal SupervisionCode0
Improving Variational Autoencoders with Density Gap-based RegularizationCode0
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallCode4
Blank Collapse: Compressing CTC emission for the faster decodingCode0
A Simple, Yet Effective Approach to Finding Biases in Code Generation0
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position SelectorCode0
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic ChangeCode1
Generating Sequences by Learning to Self-Correct0
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection TaskCode0
When Language Model Meets Private LibraryCode2
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain0
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained TransformersCode7
Emergent Linguistic Structures in Neural Networks are FragileCode0
CodeEditor: Learning to Edit Source Code with Pre-trained ModelsCode0
Artificial intelligence in government: Concepts, standards, and a unified frameworkCode0
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular ControlCode1
Tables to LaTeX: structure and content extraction from scientific tables0
L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep LearningCode1
Modular Hybrid Autoregressive Transducer0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text0
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model0
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detectionCode0
Differentiable Data Augmentation for Contrastive Sentence Representation LearningCode1
NTULM: Enriching Social Media Text Representations with Non-Textual Units0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models0
RoChBert: Towards Robust BERT Fine-tuning for ChineseCode1
Modeling structure-building in the brain with CCG parsing and large language models0
Leveraging Label Correlations in a Multi-label Setting: A Case Study in EmotionCode1
You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LMCode0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance0
Feature Engineering vs BERT on Twitter Data0
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention0
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless TrainingCode1
Simulating realistic speech overlaps improves multi-talker ASR0
Nearest Neighbor Language Models for Stylistic Controllable Generation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified