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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14101–14150 of 17610 papers

TitleStatusHype
Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models—0
BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian—0
Detecting over/under-translation errors for determining adequacy in human translations—0
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
Few-shot learning through contextual data augmentationCode0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition—0
Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays—0
AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation—0
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract MeaningCode0
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
BART based semantic correction for Mandarin automatic speech recognition system—0
Correcting Automated and Manual Speech Transcription Errors using Warped Language Models—0
An Approach to Improve Robustness of NLP Systems against ASR Errors—0
Visual Grounding Strategies for Text-Only Natural Language Processing—0
K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining—0
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2—0
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages—0
Hallucination of speech recognition errors with sequence to sequence learning—0
Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling—0
Nutri-bullets: Summarizing Health Studies by Composing SegmentsCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation—0
Set-to-Sequence Methods in Machine Learning: a Review—0
Towards Few-Shot Fact-Checking via Perplexity—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Value-aware Approximate AttentionCode0
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words ModelCode0
Advancing RNN Transducer Technology for Speech Recognition—0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
Claim Verification using a Multi-GAN based Model—0
Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation—0
Optimal Embedding Calibration for Symbolic Music Similarity—0
Improving Diversity of Neural Text Generation via Inverse Probability Weighting—0
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation—0
Evaluation of Morphological Embeddings for English and Russian Languages—0
On Improving Deep Learning Trace Analysis with System Call Arguments—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification—0
MTLHealth: A Deep Learning System for Detecting Disturbing Content in Student Essays—0
Extracting Semantic Process Information from the Natural Language in Event Logs—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Random Feature Attention—0
University of Copenhagen Participation in TREC Health Misinformation Track 2020—0
Unsupervised Word Segmentation with Bi-directional Neural Language ModelCode0
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics—0
Long Document Summarization in a Low Resource Setting using Pretrained Language Models—0
Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines—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