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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 32513300 of 17610 papers

TitleStatusHype
KinyaBERT: a Morphology-aware Kinyarwanda Language ModelCode1
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training DataCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
Pseudo-Q: Generating Pseudo Language Queries for Visual GroundingCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
ReACC: A Retrieval-Augmented Code Completion FrameworkCode1
Representation Learning for Resource-Constrained Keyphrase GenerationCode1
Do Language Models Plagiarize?Code1
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing AnomaliesCode1
Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC DomainCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State TrackingCode1
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language ModelsCode1
Mukayese: Turkish NLP Strikes BackCode1
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text RepresentationCode1
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification TasksCode1
Logical Fallacy DetectionCode1
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence LearningCode1
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support ConversationCode1
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech RecognitionCode1
Transformer Quality in Linear TimeCode1
Interpreting Language Models with Contrastive ExplanationsCode1
cosFormer: Rethinking Softmax in AttentionCode1
LAMP: Extracting Text from Gradients with Language Model PriorsCode1
Should You Mask 15% in Masked Language Modeling?Code1
CAREER: A Foundation Model for Labor Sequence DataCode1
Neighborhood Contrastive Learning for Scientific Document Representations with Citation EmbeddingsCode1
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming SequencesCode1
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of AttentionCode1
Topic Discovery via Latent Space Clustering of Pretrained Language Model RepresentationsCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language ModelingCode1
Red Teaming Language Models with Language ModelsCode1
Unified Scaling Laws for Routed Language ModelsCode1
What Has Been Enhanced in my Knowledge-Enhanced Language Model?Code1
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage LearningCode1
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme ModelsCode1
Learning To Recognize Procedural Activities with Distant SupervisionCode1
Korean-Specific Dataset for Table Question AnsweringCode1
Kformer: Knowledge Injection in Transformer Feed-Forward LayersCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
Accurate identification of bacteriophages from metagenomic data using TransformerCode1
Improving Mandarin End-to-End Speech Recognition with Word N-gram Language ModelCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse GateCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Visual Semantics Allow for Textual Reasoning Better in Scene Text RecognitionCode1
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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