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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 32013250 of 17610 papers

TitleStatusHype
Unsupervised Dependency Graph NetworkCode1
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak SupervisionCode1
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text ClassificationCode1
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response GenerationCode1
GypSum: Learning Hybrid Representations for Code SummarizationCode1
The Causal News Corpus: Annotating Causal Relations in Event Sentences from NewsCode1
Which Discriminator for Cooperative Text Generation?Code1
Emotion-Aware Transformer Encoder for Empathetic Dialogue GenerationCode1
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for DialoguesCode1
KALA: Knowledge-Augmented Language Model AdaptationCode1
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue GenerationCode1
L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language ModelsCode1
StableMoE: Stable Routing Strategy for Mixture of ExpertsCode1
Contrastive Learning with Hard Negative Entities for Entity Set ExpansionCode1
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment AnalysisCode1
Evaluation Benchmarks for Spanish Sentence RepresentationsCode1
Improving Passage Retrieval with Zero-Shot Question GenerationCode1
Generative power of a protein language model trained on multiple sequence alignmentsCode1
GPT-NeoX-20B: An Open-Source Autoregressive Language ModelCode1
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot ClassificationCode1
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-TuningCode1
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text GenerationCode1
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?Code1
A Generative Language Model for Few-shot Aspect-Based Sentiment AnalysisCode1
Contextual Representation Learning beyond Masked Language ModelingCode1
BioBART: Pretraining and Evaluation of A Biomedical Generative Language ModelCode1
RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text miningCode1
IterVM: Iterative Vision Modeling Module for Scene Text RecognitionCode1
Structure-aware Protein Self-supervised LearningCode1
SecureBERT: A Domain-Specific Language Model for CybersecurityCode1
POS-BERT: Point Cloud One-Stage BERT Pre-TrainingCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
Feature Structure Distillation with Centered Kernel Alignment in BERT TransferringCode1
Monarch: Expressive Structured Matrices for Efficient and Accurate TrainingCode1
SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing TasksCode1
WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language ModelsCode1
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language ModelCode1
Linking Emergent and Natural Languages via Corpus TransferCode1
Mix and Match: Learning-free Controllable Text Generation using Energy Language ModelsCode1
What to Hide from Your Students: Attention-Guided Masked Image ModelingCode1
HOP: History-and-Order Aware Pre-training for Vision-and-Language NavigationCode1
Language modeling via stochastic processesCode1
TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language ProcessingCode1
Self-Consistency Improves Chain of Thought Reasoning in Language ModelsCode1
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge DistillationCode1
How does the pre-training objective affect what large language models learn about linguistic properties?Code1
Dependency-based Mixture Language ModelsCode1
On Robust Prefix-Tuning for Text ClassificationCode1
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet ExtractionCode1
Show:102550
← PrevPage 65 of 353Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified