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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13251–13300 of 17610 papers

TitleStatusHype
FastTrees: Parallel Latent Tree-Induction for Faster Sequence EncodingCode0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—0
Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress—0
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets—0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception—0
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning—0
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model—0
Linking-Enhanced Pre-Training for Table Semantic Parsing—0
Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application—0
Bootstrapping Text Anonymization Models with Distant Supervision—0
Error-Correcting Codes For Approximate Neural Sequence Prediction—0
BERT got a Date: Introducing Transformers to Temporal Tagging—0
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction—0
Enhancing Robustness of Pre-trained Language Model with Lexical Simplification—0
Contextual Representation Learning beyond Masked Language Modeling—0
Better Language Model with Hypernym Class Prediction—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
Autoregressive Language Model for Zero-shot Constrained Keyphrase Generation—0
End-to-end Task-oriented Dialog Policy Learning based on Pre-trained Language Model—0
End-to-end Reference-free Single-document Summary Quality Assessment—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine Translation—0
Contrastive Learning for Low Resource Machine Translation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Controllable Natural Language Generation with Contrastive Prefixes—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Invariant Language Modeling—0
Extracting and Inferring Personal Attributes from Dialogue—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Accurate, yet Inconsistent? Consistency Analysis on Language Models—0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference—0
Integrated Semantic and Phonetic Post-correction for Chinese Speech RecognitionCode0
Can Language Models Be Specific? How?—0
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog—0
Freezing the Pivot for Triangular Machine Translation—0
Compressing Sentence Representation via Homomorphic Projective Distillation—0
gaBERT — an Irish Language Model—0
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis—0
Generated Knowledge Prompting for Commonsense Reasoning—0
Improving Unsupervised Sentence Simplification Using Fine-Tuned Masked Language Models—0
Generative Prompt Tuning for Relation Classification—0
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning—0
Adaptive Testing and Debugging of NLP Models—0
ANNA: Enhanced Language Representation for Question Answering—0
CINO: A Chinese Minority Pre-trained Language Model—0
GLM: General Language Model Pretraining with Autoregressive Blank Infilling—0
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method—0
Composing Structure-Aware Batches for Pairwise Sentence Classification—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