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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14501–14550 of 17610 papers

TitleStatusHype
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model—0
Contextual BERT: Conditioning the Language Model Using a Global State—0
Fusion Models for Improved Visual Captioning—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
Multitask Training with Text Data for End-to-End Speech Recognition—0
Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection—0
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition—0
Accelerating Training of Transformer-Based Language Models with Progressive Layer DroppingCode0
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer—0
Dutch Humor Detection by Generating Negative Examples—0
Probing Task-Oriented Dialogue Representation from Language Models—0
Large Scale Legal Text Classification Using Transformer Models—0
Revisiting Neural Language Modelling with Syllables—0
Open-Domain Dialogue Generation Based on Pre-trained Language Models—0
Unsupervised Paraphrasing with Pretrained Language Models—0
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language ModelsCode0
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model—0
CLPLM: Character Level Pretrained Language Model for Extracting Support Phrases for Sentiment Labels—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Concealed Data Poisoning Attacks on NLP Models—0
DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries—0
HateBERT: Retraining BERT for Abusive Language Detection in EnglishCode0
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer—0
ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding—0
Multilingual BERT Post-Pretraining Alignment—0
Robust Document Representations using Latent Topics and Metadata—0
On the Transformer Growth for Progressive BERT Training—0
Pre-training with Meta Learning for Chinese Word Segmentation—0
Towards Fully Bilingual Deep Language ModelingCode0
The Turking Test: Can Language Models Understand Instructions?—0
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling—0
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus—0
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets—0
Not all parameters are born equal: Attention is mostly what you needCode0
Limitations of Autoregressive Models and Their Alternatives—0
How Phonotactics Affect Multilingual and Zero-shot ASR PerformanceCode0
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval—0
Incorporating Stylistic Lexical Preferences in Generative Language Models—0
ConVEx: Data-Efficient and Few-Shot Slot Labeling—0
UniCase -- Rethinking Casing in Language Models—0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering—0
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle—0
Individual corpora predict fast memory retrieval during reading—0
A Benchmark for Lease Contract Review—0
Cue Me In: Content-Inducing Approaches to Interactive Story Generation—0
PROP: Pre-training with Representative Words Prediction for Ad-hoc RetrievalCode0
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions—0
SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
Towards Automatic Online Hate Speech Intervention Generation using Pretrained Language Model—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