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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13551–13600 of 17610 papers

TitleStatusHype
Span Labeling Approach for Vietnamese and Chinese Word Segmentation—0
Improving Punctuation Restoration for Speech Transcripts via External Data—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
A Study on Contextualized Language Modeling for Machine Reading Comprehension—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens—0
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian PoetryCode0
Deep Neural Compression Via Concurrent Pruning and Self-Distillation—0
Focused Contrastive Training for Test-based Constituency Analysis—0
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling—0
Collaborative Storytelling with Human Actors and AI Narrators—0
Improving Non-Autoregressive Translation Models Without Distillation—0
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief—0
Hierarchical Character Tagger for Short Text Spelling Error Correction—0
A Dot Product Attention Free Transformer—0
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
Analyzing the Implicit Position Encoding Ability of Transformer Decoder—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
BANANA: a Benchmark for the Assessment of Neural Architectures for Nucleic Acids—0
DictFormer: Tiny Transformer with Shared Dictionary—0
Image BERT Pre-training with Online Tokenizer—0
A Step-Wise Weighting Approach for Controllable Text Generation—0
Short-term memory in neural language models—0
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents—0
Topic Aware Neural Language Model: Domain Adaptation of Unconditional Text Generation Models—0
Offline Reinforcement Learning for Large Scale Language Action Spaces—0
Language Model Pre-training Improves Generalization in Policy Learning—0
Sparse Attention with Learning to Hash—0
Self-Distilled Pruning Of Neural Networks—0
Selective Token Generation for Few-shot Language Modeling—0
SGORNN: Combining Scalar Gates and Orthogonal Constraints in Recurrent Networks—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
TransTCN: An Attention-based TCN Framework for Sequential Modeling—0
Rethinking Client Reweighting for Selfish Federated Learning—0
On Reward Maximization and Distribution Matching for Fine-Tuning Language Models—0
Pretrained Language Model in Continual Learning: A Comparative Study—0
Not-so fine-tuning: Measures of Common Sense for Language Models—0
Private Language Model Adaptation for Speech Recognition—0
FQuAD2.0: French Question Answering and knowing that you know nothing—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Language Model Priming for Cross-Lingual Event Extraction—0
Predicting Attention Sparsity in Transformers—0
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling—0
A Proposal of Automatic Error Correction in Text—0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
Identification of Enzymatic Active Sites with Unsupervised Language Modeling—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