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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12751–12800 of 17610 papers

TitleStatusHype
Automatic Learning of Subword Dependent Model Scales—0
NormFormer: Improved Transformer Pretraining with Extra Normalization—0
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic OptimizationCode1
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes—0
GNN-LM: Language Modeling based on Global Contexts via GNNCode1
Reminding the Incremental Language Model via Data-Free Self-Distillation—0
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens—0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation—0
xGQA: Cross-Lingual Visual Question Answering—0
DEMix Layers: Disentangling Domains for Modular Language Modeling—0
Echo-Attention: Attend Once and Get N Attentions for Free—0
A Novel Metric for Evaluating Semantics PreservationCode0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models—0
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language ModelsCode1
Hydra: A System for Large Multi-Model Deep LearningCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
ASR4REAL: An extended benchmark for speech models—0
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languagesCode0
Invariant Language ModelingCode1
Prix-LM: Pretraining for Multilingual Knowledge Base ConstructionCode0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
Sharpness-Aware Minimization Improves Language Model Generalization—0
Leveraging Knowledge in Multilingual Commonsense Reasoning—0
Improving Transformers with Probabilistic Attention KeysCode1
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
DS-TOD: Efficient Domain Specialization for Task Oriented DialogCode0
Coherence boosting: When your pretrained language model is not paying enough attentionCode1
Generated Knowledge Prompting for Commonsense ReasoningCode1
Control Prefixes for Parameter-Efficient Text GenerationCode1
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language ModelsCode1
Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue—0
Meta-learning via Language Model In-context TuningCode1
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of ColorCode0
Multitask Prompted Training Enables Zero-Shot Task GeneralizationCode2
Kronecker Decomposition for GPT Compression—0
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Crisis Domain Adaptation Using Sequence-to-sequence TransformersCode0
Intent-based Product Collections for E-commerce using Pretrained Language Models—0
Sparks: Inspiration for Science Writing using Language Models—0
Symbolic Knowledge Distillation: from General Language Models to Commonsense ModelsCode1
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models—0
Spoken ObjectNet: A Bias-Controlled Spoken Caption DatasetCode0
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and TasksCode2
MIMICause: Representation and automatic extraction of causal relation types from clinical notes—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model TuningCode1
bert2BERT: Towards Reusable Pretrained Language Models—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