Copertina

MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ling, Yaobin, Jiang, Xiaoqian, Kim, Yejin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks
di: Zhou, Youran, et al.
Pubblicazione: (2025)

Generating Realistic Tabular Data with Large Language Models
di: Nguyen, Dang, et al.
Pubblicazione: (2024)

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
di: Alshantti, Abdallah, et al.
Pubblicazione: (2023)

EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
di: Kim, Jinhee, et al.
Pubblicazione: (2024)

MALLM: Multi-Agent Large Language Models Framework
di: Becker, Jonas, et al.
Pubblicazione: (2025)

ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation
di: EskandariNasab, MohammadReza, et al.
Pubblicazione: (2024)

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models
di: Sidorenko, Andrey
Pubblicazione: (2025)

MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning
di: Kim, Wall, et al.
Pubblicazione: (2026)

Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
di: Ramachandranpillai, Resmi, et al.
Pubblicazione: (2024)

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
di: Li, Zuqing, et al.
Pubblicazione: (2025)

DSF-GAN: DownStream Feedback Generative Adversarial Network
di: Perets, Oriel, et al.
Pubblicazione: (2024)

Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
di: He, Zhipeng, et al.
Pubblicazione: (2025)

Large Language Models Engineer Too Many Simple Features For Tabular Data
di: Küken, Jaris, et al.
Pubblicazione: (2024)

KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
di: Wei, Chuheng, et al.
Pubblicazione: (2024)

SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification
di: Sharifrazi, Danial, et al.
Pubblicazione: (2026)

Multi-level Diagnosis and Evaluation for Robust Tabular Feature Engineering with Large Language Models
di: Lim, Yebin, et al.
Pubblicazione: (2025)

Transfer Learning of Tabular Data by Finetuning Large Language Models
di: Rabbani, Shourav B., et al.
Pubblicazione: (2025)

Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation
di: Tang, Haocheng, et al.
Pubblicazione: (2025)

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning
di: EskandariNasab, MohammadReza, et al.
Pubblicazione: (2024)

An Interpretable and Scalable Framework for Evaluating Large Language Models
di: Qu, Xinhao, et al.
Pubblicazione: (2026)

TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
di: He, Zhipeng, et al.
Pubblicazione: (2025)

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
di: Qiao, Yiran, et al.
Pubblicazione: (2024)

TabularQGAN: A Quantum Generative Model for Tabular Data
di: Bhardwaj, Pallavi, et al.
Pubblicazione: (2025)

Large Scale Transfer Learning for Tabular Data via Language Modeling
di: Gardner, Josh, et al.
Pubblicazione: (2024)

A Model of Causal Explanation on Neural Networks for Tabular Data
di: Isozaki, Takashi, et al.
Pubblicazione: (2025)

CoLoR-GAN: Continual Few-Shot Learning with Low-Rank Adaptation in Generative Adversarial Networks
di: Ali, Munsif, et al.
Pubblicazione: (2025)

Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
di: Nam, Jaehyun, et al.
Pubblicazione: (2024)

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining
di: Yang, Yazheng, et al.
Pubblicazione: (2024)

CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network
di: Zhao, Jiangwei, et al.
Pubblicazione: (2021)

ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation
di: Li, Peiran, et al.
Pubblicazione: (2026)

HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
di: Nadera, Noujoud, et al.
Pubblicazione: (2026)

Interpretable Graph Neural Networks for Tabular Data
di: Alkhatib, Amr, et al.
Pubblicazione: (2023)

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
di: Zhang, Jiayu, et al.
Pubblicazione: (2025)

Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
di: Bordt, Sebastian, et al.
Pubblicazione: (2024)

TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
di: Gao, Weihao, et al.
Pubblicazione: (2024)

Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
di: Zhang, Liang, et al.
Pubblicazione: (2025)

The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Training
di: Shim, Joo Yong, et al.
Pubblicazione: (2024)

RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
di: Iotti, Marcello, et al.
Pubblicazione: (2025)

Enhancing Stability in Training Conditional Generative Adversarial Networks via Selective Data Matching
di: Kong, Kyeongbo, et al.
Pubblicazione: (2021)

Tabular Data Generation using Binary Diffusion
di: Kinakh, Vitaliy, et al.
Pubblicazione: (2024)