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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14301–14350 of 17610 papers

TitleStatusHype
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration—0
A review of on-device fully neural end-to-end automatic speech recognition algorithms—0
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval—0
MiniVLM: A Smaller and Faster Vision-Language Model—0
Mapping the Timescale Organization of Neural Language Models—0
CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation IdentificationCode0
AffectON: Incorporating Affect Into Dialog Generation—0
Morphology Matters: A Multilingual Language Modeling AnalysisCode0
Multi-Sense Language Modelling—0
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR—0
Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos—0
Cross-lingual Transfer of Abstractive Summarizer to Less-resource Language—0
Parameter Efficient Multimodal Transformers for Video Representation Learning—0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
Playing Text-Based Games with Common Sense—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
GottBERT: a pure German Language Model—0
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning—0
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition—0
Federated Learning for Personalized Humor Recognition—0
Cross-Loss Influence Functions to Explain Deep Network RepresentationsCode0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN—0
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory—0
Distill and Replay for Continual Language Learning—0
ELMo-NB at SemEval-2020 Task 7: Assessing Sense of Humor in EditedNews Headlines Using ELMo and NB—0
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted AttentionCode0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation—0
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model—0
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature—0
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown DetectionCode0
Context-Aware Text Normalisation for Historical Dialects—0
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation—0
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements—0
Domain Transfer based Data Augmentation for Neural Query Translation—0
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities—0
A Neural Local Coherence Analysis Model for Clarity Text Scoring—0
Communication-Efficient Federated Distillation—0
Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases—0
Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language ModelsCode0
Investigating Learning Dynamics of BERT Fine-Tuning—0
Exploring the zero-shot limit of FewRelCode0
Composing Byte-Pair Encodings for Morphological Sequence ClassificationCode0
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection—0
A Sentiment-annotated Dataset of English Causal ConnectivesCode0
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification—0
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing—0
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion—0
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning—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