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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language ModelingCode1
Projection of Turn Completion in Incremental Spoken Dialogue Systems—0
Getting to Production with Few-shot Natural Language Generation Models—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
XLM-E: Cross-lingual Language Model Pre-training via ELECTRACode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models—0
What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval—0
R-Drop: Regularized Dropout for Neural NetworksCode1
Stabilizing Equilibrium Models by Jacobian RegularizationCode1
SymbolicGPT: A Generative Transformer Model for Symbolic RegressionCode1
Visual Conceptual Blending with Large-scale Language and Vision Models—0
Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models—0
Multimodal Few-Shot Learning with Frozen Language Models—0
Language Models are Good Translators—0
Learning to Sample Replacements for ELECTRA Pre-Training—0
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model—0
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus—0
Multi-objective Asynchronous Successive HalvingCode3
Clinical Named Entity Recognition using Contextualized Token Representations—0
CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot—0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
Combining Analogy with Language Models for Knowledge ExtractionCode0
Prompt Tuning or Fine-Tuning - Investigating Relational Knowledge in Pre-Trained Language ModelsCode0
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training—0
End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge ManagementCode0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Ad Text Classification with Transformer-Based Natural Language Processing Methods—0
Membership Inference on Word Embedding and Beyond—0
Secure Distributed Training at ScaleCode1
CLIP2Video: Mastering Video-Text Retrieval via Image CLIPCode1
A Discriminative Entity-Aware Language Model for Virtual Assistants—0
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic lossCode0
RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual WordsCode1
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
Transitional Adaptation of Pretrained Models for Visual Storytelling—0
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets—0
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System—0
Label prompt for multi-label text classification—0
SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge GraphsCode1
Learning to Complete Code with Sketches—0
Distributed Deep Learning in Open CollaborationsCode1
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition—0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelsCode1
Golos: Russian Dataset for Speech ResearchCode1
On Anytime Learning at MacroscaleCode0
LoRA: Low-Rank Adaptation of Large Language ModelsCode2
Augmented Neural Story Generation with Commonsense Inference—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