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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12151–12200 of 17610 papers

TitleStatusHype
Towards Zero-Shot Code-Switched Speech Recognition—0
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model—0
Numerical Optimizations for Weighted Low-rank Estimation on Language Model—0
Training Vision-Language Models with Less Bimodal SupervisionCode0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization—0
Machine learning can guide experimental approaches for protein digestibility estimations—0
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language InstructionsCode0
NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach—0
Language Model Based Chinese Handwriting Address Recognition—0
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation—0
The future is different: Large pre-trained language models fail in prediction tasks—0
A Quantitative Analysis of Comparison of Emoji Sentiment: Taiwan Mandarin Users and English Users—0
Improving Variational Autoencoders with Density Gap-based RegularizationCode0
HanTrans: An Empirical Study on Cross-Era Transferability of Chinese Pre-trained Language ModelCode0
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding—0
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain—0
Modular Hybrid Autoregressive Transducer—0
Tables to LaTeX: structure and content extraction from scientific tables—0
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection TaskCode0
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position SelectorCode0
Generating Sequences by Learning to Self-Correct—0
Blank Collapse: Compressing CTC emission for the faster decodingCode0
CodeEditor: Learning to Edit Source Code with Pre-trained ModelsCode0
Emergent Linguistic Structures in Neural Networks are FragileCode0
Artificial intelligence in government: Concepts, standards, and a unified frameworkCode0
A Simple, Yet Effective Approach to Finding Biases in Code Generation—0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts—0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text—0
NTULM: Enriching Social Media Text Representations with Non-Textual Units—0
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detectionCode0
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model—0
Feature Engineering vs BERT on Twitter Data—0
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention—0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LMCode0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models—0
Modeling structure-building in the brain with CCG parsing and large language models—0
Nearest Neighbor Language Models for Stylistic Controllable Generation—0
Learning Joint Representation of Human Motion and Language—0
Simulating realistic speech overlaps improves multi-talker ASR—0
SAN: a robust end-to-end ASR model architecture—0
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge—0
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model—0
Evaluating context-invariance in unsupervised speech representationsCode0
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence LabelingCode0
Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?—0
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks—0
A Robust Bias Mitigation Procedure Based on the Stereotype Content ModelCode0
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design—0
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language ModelingCode0
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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