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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1525115300 of 17610 papers

TitleStatusHype
SCALE: A Scalable Language Engineering ToolkitCode0
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense RetrievalCode0
One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial SupportCode0
SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human InterventionCode0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural InformationCode0
Pre-Training a Graph Recurrent Network for Language RepresentationCode0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Pre-trained Language Model for Biomedical Question AnsweringCode0
Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller Language ModelsCode0
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language ModelsCode0
Preserving Generalization of Language models in Few-shot Continual Relation ExtractionCode0
Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corporaCode0
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak AttacksCode0
Preparation and Usage of Xhosa Lexicographical Data for a Multilingual, Federated EnvironmentCode0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
Large Language Model Recall Uncertainty is Modulated by the Fan EffectCode0
Structured Content Preservation for Unsupervised Text Style TransferCode0
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User ReviewsCode0
Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ GuidelinesCode0
Large Language Model-Guided Prediction Toward Quantum Materials SynthesisCode0
Train-Attention: Meta-Learning Where to Focus in Continual Knowledge LearningCode0
Preliminary Study on Incremental Learning for Large Language Model-based Recommender SystemsCode0
JavaBERT: Training a transformer-based model for the Java programming languageCode0
Structured Like a Language Model: Analysing AI as an Automated SubjectCode0
A Federated Framework for LLM-based RecommendationCode0
Scaling Open-Vocabulary Object DetectionCode0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
Molecular Facts: Desiderata for Decontextualization in LLM Fact VerificationCode0
Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language ModelsCode0
Preference Optimization for Molecular Language ModelsCode0
Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM InteractionsCode0
Predictive Querying for Autoregressive Neural Sequence ModelsCode0
LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent EcosystemCode0
Prediction-Powered Ranking of Large Language ModelsCode0
Structured Sequence Modeling with Graph Convolutional Recurrent NetworksCode0
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and GuardrailsCode0
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language ModelCode0
Predicting Class Distribution Shift for Reliable Domain Adaptive Object DetectionCode0
Predicting 3D Human Dynamics from VideoCode0
Predefined Sparseness in Recurrent Sequence ModelsCode0
Scaling Up Probabilistic Circuits by Latent Variable DistillationCode0
The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsCode0
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix QualityCode0
Precise Task Formalization Matters in Winograd Schema EvaluationsCode0
PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual AlignmentCode0
Practical Text Classification With Large Pre-Trained Language ModelsCode0
LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language ModelCode0
Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement LearningCode0
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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