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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14251–14300 of 17610 papers

TitleStatusHype
Pretrain Knowledge-Aware Language Models—0
Translation Memory Guided Neural Machine Translation—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Syntactic Relevance XLNet Word Embedding Generation in Low-Resource Machine Translation—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
TaskSet: A Dataset of Optimization TasksCode0
Neural spatio-temporal reasoning with object-centric self-supervised learning—0
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling—0
Subformer: A Parameter Reduced Transformer—0
Non-iterative Parallel Text Generation via Glancing Transformer—0
Synthesizer: Rethinking Self-Attention for Transformer Models—0
ROMUL: Scale Adaptative Population Based Training—0
Task-Agnostic and Adaptive-Size BERT Compression—0
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS—0
Sensei: Self-Supervised Sensor Name SegmentationCode0
Text Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings—0
Towards Practical Second Order Optimization for Deep Learning—0
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing—0
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training—0
On the use of linguistic similarities to improve Neural Machine Translation for African Languages—0
Transformer-QL: A Step Towards Making Transformer Network Quadratically Large—0
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration—0
Universal Sentence Representations Learning with Conditional Masked Language Model—0
Verb Knowledge Injection for Multilingual Event Processing—0
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders—0
Directed Beam Search: Plug-and-Play Lexically Constrained Language GenerationCode0
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse RelationsCode0
ERNIE-Doc: A Retrospective Long-Document Modeling TransformerCode0
Studying Strategically: Learning to Mask for Closed-book QA—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Reservoir Transformers—0
Enhancing Pre-trained Language Model with Lexical Simplification—0
Can Sequence-to-Sequence Models Crack Substitution Ciphers?—0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces—0
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
General Mechanism of Evolution Shared by Proteins and WordsCode0
Universal Sentence Representation Learning with Conditional Masked Language Model—0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Contextual Temperature for Language Modeling—0
A Context Aware Approach for Generating Natural Language AttacksCode0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—0
SubICap: Towards Subword-informed Image Captioning—0
Code Switching Language Model Using Monolingual Training Data—0
Pre-Training a Language Model Without Human Language—0
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition—0
An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations—0
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection—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