SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Sustainable Modular Debiasing of Language Models—0
RefineCap: Concept-Aware Refinement for Image Captioning—0
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form StoriesCode0
NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction—0
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story BooksCode1
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Text-Free Prosody-Aware Generative Spoken Language Modeling—0
Generate & Rank: A Multi-task Framework for Math Word Problems—0
Infusing Future Information into Monotonic Attention Through Language Models—0
Sequential Attention Module for Natural Language Processing—0
Text-to-Table: A New Way of Information ExtractionCode1
PermuteFormer: Efficient Relative Position Encoding for Long SequencesCode1
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
You should evaluate your language model on marginal likelihood over tokenisations—0
Learning Hierarchical Structures with Differentiable Nondeterministic StacksCode1
Teaching Autoregressive Language Models Complex Tasks By DemonstrationCode0
Data Efficient Masked Language Modeling for Vision and LanguageCode1
Frustratingly Simple Pretraining Alternatives to Masked Language ModelingCode1
No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge—0
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT—0
Finetuned Language Models Are Zero-Shot LearnersCode3
Multimodal Conditionality for Natural Language Generation—0
LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training—0
Skim-Attention: Learning to Focus via Document LayoutCode0
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation—0
Imposing Relation Structure in Language-Model Embeddings Using Contrastive LearningCode1
An Empirical Exploration in Quality Filtering of Text Data—0
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation—0
Towards a Language Model for Temporal Commonsense Reasoning—0
Neural Borrowing Detection with Monolingual Lexical Models—0
On Reducing Repetition in Abstractive Summarization—0
Watching a Language Model Learning Chess—0
Unsupervised Text Style Transfer with Content Embeddings—0
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data—0
BPoMP: The Benchmark of Poetic Minimal Pairs – Limericks, Rhyme, and Narrative Coherence—0
Behavior of Modern Pre-trained Language Models Using the Example of Probing Tasks—0
Improving Character-Aware Neural Language Model by Warming up Character Encoder under Skip-gram Architecture—0
Domain-Specific Japanese ELECTRA Model Using a Small Corpus—0
On the Contribution of Per-ICD Attention Mechanisms to Classify Health Records in Languages with Fewer Resources than EnglishCode4
Split-and-Rephrase in a Cross-Lingual Manner: A Complete Pipeline—0
Low-Resource ASR with an Augmented Language Model—0
IRCologne at GermEval 2021: Toxicity Classification—0
ReMeDi: Resources for Multi-domain, Multi-service, Medical DialoguesCode1
Masked Adversarial Generation for Neural Machine Translation—0
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect ExtractionCode1
-former: Infinite Memory TransformerCode1
Does Knowledge Help General NLU? An Empirical Study—0
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
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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