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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 46514700 of 17610 papers

TitleStatusHype
Decoding fMRI Data into Captions using Prefix Language ModelingCode0
From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics SystemsCode0
From neighborhood to parenthood: the advantages of dependency representation over bigrams in Brown clusteringCode0
Can Generative LLMs Create Query Variants for Test Collections? An Exploratory StudyCode0
I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR DiagnosesCode0
Can Github issues be solved with Tree Of Thoughts?Code0
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMsCode0
An Exploratory Investigation into Code License Infringements in Large Language Model Training DatasetsCode0
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response ForecastingCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
Can Language Models Be Specific? How?Code0
Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for EducationCode0
DE-COP: Detecting Copyrighted Content in Language Models Training DataCode0
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning FrameworksCode0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue AgentsCode0
AttViz: Online exploration of self-attention for transparent neural language modelingCode0
Can Large Language Models Learn Independent Causal Mechanisms?Code0
DEEPAGÉ: Answering Questions in Portuguese about the Brazilian EnvironmentCode0
An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease PhenotypingCode0
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated ContentCode0
Vocabulary-level Memory Efficiency for Language Model Fine-tuningCode0
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion DiagnosisCode0
Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting PotCode0
Decoupled Sequence and Structure Generation for Realistic Antibody DesignCode0
ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language ModelCode0
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the WildCode0
Deeper Text Understanding for IR with Contextual Neural Language ModelingCode0
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-gramsCode0
Fusing Sentence Embeddings Into LSTM-based Autoregressive Language ModelsCode0
Deep-FSMN for Large Vocabulary Continuous Speech RecognitionCode0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
Future Language Modeling from Temporal Document HistoryCode0
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented DialogueCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney InjuryCode0
An Imitation Learning Approach to Unsupervised ParsingCode0
Deep Independently Recurrent Neural Network (IndRNN)Code0
A Comprehensive Evaluation of Quantization Strategies for Large Language ModelsCode0
Canonical and Surface Morphological Segmentation for Nguni LanguagesCode0
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical DebtCode0
Deep Learning Based Chatbot ModelsCode0
GapPredict: A Language Model for Resolving Gaps in Draft Genome AssembliesCode0
Garbage in, garbage out: Zero-shot detection of crime using Large Language ModelsCode0
Garden-Path Traversal in GPT-2Code0
Gated Word-Character Recurrent Language ModelCode0
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative ClassificationCode0
Gates Are Not What You Need in RNNsCode0
Deep Learning for Source Code Modeling and Generation: Models, Applications and ChallengesCode0
Gating Revisited: Deep Multi-layer RNNs That Can Be TrainedCode0
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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