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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13001–13050 of 17610 papers

TitleStatusHype
A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism—0
Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering—0
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale—0
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction—0
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training—0
CINO: A Chinese Minority Pre-trained Language Model—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
Controllable Natural Language Generation with Contrastive Prefixes—0
From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics SystemsCode0
Probing BERT's priors with serial reproduction chainsCode0
Oolong: Investigating What Makes Transfer Learning Hard with Controlled StudiesCode0
Korean Tokenization for Beam Search Rescoring in Speech Recognition—0
VU-BERT: A Unified framework for Visual Dialog—0
Evaluating Persian Tokenizers—0
Adaptive Discounting of Implicit Language Models in RNN-Transducers—0
StyleBERT: Chinese pretraining by font style information—0
Reward Modeling for Mitigating Toxicity in Transformer-based Language Models—0
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French—0
A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
XFBoost: Improving Text Generation with Controllable Decoders—0
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model—0
Text-Based Action-Model Acquisition for Planning—0
Misinformation Detection in Social Media Video Posts—0
Punctuation restoration in Swedish through fine-tuned KB-BERT—0
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning—0
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder—0
What Does it Mean for a Language Model to Preserve Privacy?—0
AdaPrompt: Adaptive Model Training for Prompt-based NLP—0
Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs—0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic AnalysisCode0
Differentiable N-gram Objective on Abstractive SummarizationCode0
Using a Language Model in a Kiosk Recommender System at Fast-Food Restaurants—0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Prompt-Guided Injection of Conformation to Pre-trained Protein Model—0
Ethics, Rules of Engagement, and AI: Neural Narrative Mapping Using Large Transformer Language Models—0
From Discrimination to Generation: Knowledge Graph Completion with Generative TransformerCode0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
Polyphonic pitch detection with convolutional recurrent neural networks—0
mSLAM: Massively multilingual joint pre-training for speech and text—0
Pop Quiz! Can a Large Language Model Help With Reverse Engineering?—0
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Examining Scaling and Transfer of Language Model Architectures for Machine Translation—0
Disaster Tweets Classification using BERT-Based Language Model—0
Does Transliteration Help Multilingual Language Modeling?Code0
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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