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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 35013550 of 17610 papers

TitleStatusHype
Image-guided topic modeling for interpretable privacy classificationCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning0
SciDFM: A Large Language Model with Mixture-of-Experts for Science0
Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture0
Exploring Language Model Generalization in Low-Resource Extractive QACode0
Data-Prep-Kit: getting your data ready for LLM application developmentCode4
AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking0
LangSAMP: Language-Script Aware Multilingual PretrainingCode0
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies0
Development and Validation of a Dynamic-Template-Constrained Large Language Model for Generating Fully-Structured Radiology ReportsCode0
Enhancing elusive clues in knowledge learning by contrasting attention of language modelsCode0
Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience0
Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree AwarenessCode0
EgoLM: Multi-Modal Language Model of Egocentric Motions0
EAGLE: Egocentric AGgregated Language-video Engine0
Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective0
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language ModelsCode0
Open-World Evaluation for Retrieving Diverse Perspectives0
Control Industrial Automation System with Large Language Model AgentsCode2
AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure0
Human Mobility Modeling with Limited Information via Large Language Models0
Cascade Prompt Learning for Vision-Language Model AdaptationCode3
CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches0
DualAD: Dual-Layer Planning for Reasoning in Autonomous DrivingCode1
LLM4Brain: Training a Large Language Model for Brain Video Understanding0
Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study0
BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text0
Inference-Time Language Model Alignment via Integrated Value Guidance0
Mitigating the Bias of Large Language Model EvaluationCode0
Shifting from endangerment to rebirth in the Artificial Intelligence Age: An Ensemble Machine Learning Approach for Hawrami Text ClassificationCode0
Empirical Asset Pricing with Large Language Model AgentsCode2
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions0
Large Language Model Predicts Above Normal All India Summer Monsoon Rainfall in 20240
FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text CompressionCode1
Counterfactual Token Generation in Large Language ModelsCode1
Training Language Models to Win Debates with Self-Play Improves Judge AccuracyCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Multi-objective Evolution of Heuristic Using Large Language Model0
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient ModificationCode1
Judgment of Thoughts: Courtroom of the Binary Logical Reasoning in Large Language Models0
DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene OntologiesCode0
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG ModelCode2
Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based StrategiesCode0
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability0
Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation0
Predicting Distance matrix with large language models0
Exploring the traditional NMT model and Large Language Model for chat translation0
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation0
Show:102550
← PrevPage 71 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