Saved in:
| Main Authors: | Shi, Ruxue, Gu, Hengrui, Shen, Xu, Wang, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.05744 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
by: Shi, Ruxue, et al.
Published: (2025)
by: Shi, Ruxue, et al.
Published: (2025)
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
by: Wang, Yuxin, et al.
Published: (2024)
by: Wang, Yuxin, et al.
Published: (2024)
A Comprehensive Survey of Synthetic Tabular Data Generation
by: Shi, Ruxue, et al.
Published: (2025)
by: Shi, Ruxue, et al.
Published: (2025)
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
by: Liu, Yanchen, et al.
Published: (2023)
by: Liu, Yanchen, et al.
Published: (2023)
Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
by: Yuan, Yifei, et al.
Published: (2025)
by: Yuan, Yifei, et al.
Published: (2025)
Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
by: Wu, Xiaofeng, et al.
Published: (2025)
by: Wu, Xiaofeng, et al.
Published: (2025)
Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification
by: AlMarri, Saeed, et al.
Published: (2025)
by: AlMarri, Saeed, et al.
Published: (2025)
Anomaly Detection of Tabular Data Using LLMs
by: Li, Aodong, et al.
Published: (2024)
by: Li, Aodong, et al.
Published: (2024)
Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
by: Kearney, Sophie, et al.
Published: (2025)
by: Kearney, Sophie, et al.
Published: (2025)
TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
by: Arazi, Alan, et al.
Published: (2025)
by: Arazi, Alan, et al.
Published: (2025)
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
by: Kearney, Sophie, et al.
Published: (2026)
by: Kearney, Sophie, et al.
Published: (2026)
Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation
by: Wu, Yang, et al.
Published: (2025)
by: Wu, Yang, et al.
Published: (2025)
Towards Universal Debiasing for Language Models-based Tabular Data Generation
by: Li, Tianchun, et al.
Published: (2025)
by: Li, Tianchun, et al.
Published: (2025)
Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR
by: Gu, Hengrui, et al.
Published: (2026)
by: Gu, Hengrui, et al.
Published: (2026)
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
by: Allu, Uday, et al.
Published: (2024)
by: Allu, Uday, et al.
Published: (2024)
Tabular Transfer Learning via Prompting LLMs
by: Nam, Jaehyun, et al.
Published: (2024)
by: Nam, Jaehyun, et al.
Published: (2024)
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
by: Song, Zitao, et al.
Published: (2024)
by: Song, Zitao, et al.
Published: (2024)
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis
by: An, Seunghwan, et al.
Published: (2024)
by: An, Seunghwan, et al.
Published: (2024)
Reasoning Boosts Opinion Alignment in LLMs
by: Berdoz, Frédéric, et al.
Published: (2026)
by: Berdoz, Frédéric, et al.
Published: (2026)
In-Context Explainers: Harnessing LLMs for Explaining Black Box Models
by: Kroeger, Nicholas, et al.
Published: (2023)
by: Kroeger, Nicholas, et al.
Published: (2023)
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
by: Abhyankar, Nikhil, et al.
Published: (2025)
by: Abhyankar, Nikhil, et al.
Published: (2025)
An Automatic Prompt Generation System for Tabular Data Tasks
by: Akella, Ashlesha, et al.
Published: (2024)
by: Akella, Ashlesha, et al.
Published: (2024)
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
by: Patwa, Parth, et al.
Published: (2024)
by: Patwa, Parth, et al.
Published: (2024)
Making Pre-trained Language Models Great on Tabular Prediction
by: Yan, Jiahuan, et al.
Published: (2024)
by: Yan, Jiahuan, et al.
Published: (2024)
Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
by: Cai, Hengrui, et al.
Published: (2023)
by: Cai, Hengrui, et al.
Published: (2023)
Transfer Learning of Tabular Data by Finetuning Large Language Models
by: Rabbani, Shourav B., et al.
Published: (2025)
by: Rabbani, Shourav B., et al.
Published: (2025)
GRILE: A Benchmark for Grammar Reasoning and Explanation in Romanian LLMs
by: Dumitran, Adrian-Marius, et al.
Published: (2025)
by: Dumitran, Adrian-Marius, et al.
Published: (2025)
Surrogate modeling for interpreting black-box LLMs in medical predictions
by: Han, Changho, et al.
Published: (2026)
by: Han, Changho, et al.
Published: (2026)
Text Rationalization for Robust Causal Effect Estimation
by: Zhang, Lijinghua, et al.
Published: (2025)
by: Zhang, Lijinghua, et al.
Published: (2025)
Efficient Uncertainty Estimation for LLM-based Entity Linking in Tabular Data
by: Bono, Carlo, et al.
Published: (2025)
by: Bono, Carlo, et al.
Published: (2025)
Large Scale Transfer Learning for Tabular Data via Language Modeling
by: Gardner, Josh, et al.
Published: (2024)
by: Gardner, Josh, et al.
Published: (2024)
Differentially Private Tabular Data Synthesis using Large Language Models
by: Tran, Toan V., et al.
Published: (2024)
by: Tran, Toan V., et al.
Published: (2024)
Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
by: Zhang, Zheyu, et al.
Published: (2025)
by: Zhang, Zheyu, et al.
Published: (2025)
Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
by: Taghanaki, Saeid Asgari, et al.
Published: (2025)
by: Taghanaki, Saeid Asgari, et al.
Published: (2025)
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
by: Yu, Zhongzhi, et al.
Published: (2024)
by: Yu, Zhongzhi, et al.
Published: (2024)
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
by: Yang, Yazheng, et al.
Published: (2023)
by: Yang, Yazheng, et al.
Published: (2023)
Human-LLM Collaborative Feature Engineering for Tabular Data
by: Li, Zhuoyan, et al.
Published: (2026)
by: Li, Zhuoyan, et al.
Published: (2026)
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data
by: Feng, Tao, et al.
Published: (2025)
by: Feng, Tao, et al.
Published: (2025)
LayerBoost: Layer-Aware Attention Reduction for Efficient LLMs
by: Souibgui, Mohamed Ali, et al.
Published: (2026)
by: Souibgui, Mohamed Ali, et al.
Published: (2026)
Similar Items
-
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
by: Shi, Ruxue, et al.
Published: (2025) -
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
by: Wang, Yuxin, et al.
Published: (2024) -
A Comprehensive Survey of Synthetic Tabular Data Generation
by: Shi, Ruxue, et al.
Published: (2025) -
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
by: Liu, Yanchen, et al.
Published: (2023) -
Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification
by: Yuan, Yifei, et al.
Published: (2025)