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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13751–13800 of 17610 papers

TitleStatusHype
The University of Edinburgh’s Submission to the IWSLT21 Simultaneous Translation Task—0
MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection—0
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check—0
Probing Multi-modal Machine Translation with Pre-trained Language Model—0
Multi-Lingual Question Generation with Language Agnostic Language ModelCode0
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation—0
Text-in-Context: Token-Level Error Detection for Table-to-Text GenerationCode0
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?—0
MVP-BERT: Multi-Vocab Pre-training for Chinese BERT—0
S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging—0
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference—0
基于预训练语言模型的繁体古文自动句读研究(Automatic Traditional Ancient Chinese Texts Segmentation and Punctuation Based on Pre-training Language Model)—0
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets—0
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning—0
Selecting Informative Contexts Improves Language Model Fine-tuning—0
RoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and Offense—0
Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection—0
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding—0
Time-Efficient Code Completion Model for the R Programming LanguageCode0
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER—0
Meta-Learning for Few-Shot Named Entity Recognition—0
Personalized Response Generation with Tensor Factorization—0
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text—0
Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction—0
Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction—0
Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning.—0
Using Gender- and Polarity-Informed Models to Investigate Bias—0
Unleash GPT-2 Power for Event Detection—0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—0
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers—0
DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods—0
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising—0
Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation—0
Gates Are Not What You Need in RNNsCode0
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation—0
A Comparison of Sentence-Weighting Techniques for NMT—0
AStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-Training—0
Entity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans DetectionCode0
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations—0
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation—0
CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEval—0
Decoding, Fast and Slow: A Case Study on Balancing Trade-Offs in Incremental, Character-level Pragmatic Reasoning—0
Evaluating morphological typology in zero-shot cross-lingual transfer—0
AND does not mean OR: Using Formal Languages to Study Language Models' Representations—0
IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News—0
Entity and Evidence Guided Document-Level Relation Extraction—0
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model—0
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental—0
A Targeted Assessment of Incremental Processing in Neural Language Models and Humans—0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
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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