Cover Image

LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Peng, Wang, Dongsheng, Zhao, He, Ye, Hangting, Guo, Dandan, Chang, Yi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLM Meeting Decision Trees on Tabular Data
by: Ye, Hangting, et al.
Published: (2025)

PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
by: Ye, Hangting, et al.
Published: (2024)

Deep Tabular Representation Corrector
by: Ye, Hangting, et al.
Published: (2026)

Calibrating Tabular Anomaly Detection via Optimal Transport
by: Ye, Hangting, et al.
Published: (2026)

Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
by: Shi, Ruxue, et al.
Published: (2025)

LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
by: Ye, Hangting, et al.
Published: (2025)

Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
by: Lu, Ruiying, et al.
Published: (2025)

BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
by: Wang, Yi-Siang, et al.
Published: (2026)

Few-Shot Inspired Generative Zero-Shot Learning
by: Shohag, Md Shakil Ahamed, et al.
Published: (2025)

TabImpute: Universal Zero-Shot Imputation for Tabular Data
by: Feitelberg, Jacob, et al.
Published: (2025)

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
by: Sgouropoulos, Christos, et al.
Published: (2025)

Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
by: Hu, Minyang, et al.
Published: (2024)

Quantum Diffusion Models for Few-Shot Learning
by: Wang, Ruhan, et al.
Published: (2024)

Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning
by: Huertas, Carlos
Published: (2024)

Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
by: Han, Sungwon, et al.
Published: (2024)

FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
by: Shi, Changlong, et al.
Published: (2025)

Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
by: Kearney, Sophie, et al.
Published: (2025)

A Comprehensive Survey of Synthetic Tabular Data Generation
by: Shi, Ruxue, et al.
Published: (2025)

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
by: Yuan, Yifei, et al.
Published: (2025)

Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models
by: He, Kang, et al.
Published: (2024)

ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling
by: Licht, Martin, et al.
Published: (2025)

Task-Level Contrastiveness for Cross-Domain Few-Shot Learning
by: Topollai, Kristi, et al.
Published: (2025)

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
by: Zhang, Bingjie, et al.
Published: (2025)

Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning
by: Rostami, Mohammad, et al.
Published: (2024)

From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
by: Ding, Xueying, et al.
Published: (2026)

Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
by: Kearney, Sophie, et al.
Published: (2026)

Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
by: Feng, Yinqiu, et al.
Published: (2024)

Learning Relational Tabular Data without Shared Features
by: Wu, Zhaomin, et al.
Published: (2025)

A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
by: Abdollahzadeh, Milad, et al.
Published: (2023)

LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA
by: Gude, Adrián, et al.
Published: (2025)

Representation Learning for Tabular Data: A Comprehensive Survey
by: Jiang, Jun-Peng, et al.
Published: (2025)

Learning New Tasks from a Few Examples with Soft-Label Prototypes
by: Singh, Avyav Kumar, et al.
Published: (2022)

IPEC: Test-Time Incremental Prototype Enhancement Classifier for Few-Shot Learning
by: Liao, Wenwen, et al.
Published: (2026)

FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
by: Shi, Changlong, et al.
Published: (2025)

From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
by: Liu, Yixin, et al.
Published: (2026)

Towards Zero-Shot Task-Generalizable Learning on fMRI
by: Wang, Jiyao, et al.
Published: (2025)

Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
by: Mandal, Bishwas, et al.
Published: (2024)

Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
by: Martinez-Lopez, Fernando, et al.
Published: (2024)

FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
by: Yang, Zhihan, et al.
Published: (2026)

Tied Prototype Model for Few-Shot Medical Image Segmentation
by: Kim, Hyeongji, et al.
Published: (2025)