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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13651–13700 of 17610 papers

TitleStatusHype
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces—0
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring—0
Sustainable Modular Debiasing of Language Models—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
RefineCap: Concept-Aware Refinement for Image Captioning—0
Text-Free Prosody-Aware Generative Spoken Language Modeling—0
Sequential Attention Module for Natural Language Processing—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Generate & Rank: A Multi-task Framework for Math Word Problems—0
Infusing Future Information into Monotonic Attention Through Language Models—0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
You should evaluate your language model on marginal likelihood over tokenisations—0
Teaching Autoregressive Language Models Complex Tasks By DemonstrationCode0
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
Skim-Attention: Learning to Focus via Document LayoutCode0
Multimodal Conditionality for Natural Language Generation—0
LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training—0
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation—0
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
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
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data—0
IRCologne at GermEval 2021: Toxicity Classification—0
Domain-Specific Japanese ELECTRA Model Using a Small Corpus—0
Does Knowledge Help General NLU? An Empirical Study—0
Improving Character-Aware Neural Language Model by Warming up Character Encoder under Skip-gram Architecture—0
Unsupervised Text Style Transfer with Content Embeddings—0
Watching a Language Model Learning Chess—0
Towards a Language Model for Temporal Commonsense Reasoning—0
On Reducing Repetition in Abstractive Summarization—0
Low-Resource ASR with an Augmented Language Model—0
Neural Borrowing Detection with Monolingual Lexical Models—0
Split-and-Rephrase in a Cross-Lingual Manner: A Complete Pipeline—0
Masked Adversarial Generation for Neural Machine Translation—0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
The effects of data size on Automated Essay Scoring engines—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching—0
Injecting Text in Self-Supervised Speech Pretraining—0
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors—0
Improving callsign recognition with air-surveillance data in air-traffic communication—0
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network—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