{"total":23,"rows":[{"id":"2307.03393","title":"Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs","created":"2023-07-07","cited_by_count":274,"area":null},{"id":"2404.09491","title":"Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning","created":"2024-04-15","cited_by_count":81,"area":null},{"id":"2407.14614","title":"Evaluating language models as risk scores","created":"2024-07-19","cited_by_count":27,"area":null},{"id":"2402.00160","title":"Emergency Department Decision Support using Clinical Pseudo-notes","created":"2024-01-31","cited_by_count":22,"area":null},{"id":"2408.10548","title":"Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution","created":"2024-08-20","cited_by_count":19,"area":null},{"id":"2403.20208","title":"Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining","created":"2024-03-29","cited_by_count":12,"area":null},{"id":"2403.05681","title":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2412.04642","title":"Improving LLM Group Fairness on Tabular Data via In-Context Learning","created":"2024-12-05","cited_by_count":6,"area":null},{"id":"2512.16433","title":"Emergent Bias and Fairness in Multi-Agent Decision Systems","created":"2025-12-18","cited_by_count":5,"area":null},{"id":"2601.11311","title":"FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning","created":"2026-01-16","cited_by_count":5,"area":null},{"id":"2501.06863","title":"Transfer Learning of Tabular Data by Finetuning Large Language Models","created":"2025-01-12","cited_by_count":4,"area":null},{"id":"2502.02672","title":"Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes","created":"2025-02-04","cited_by_count":4,"area":null},{"id":"2505.05237","title":"Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning","created":"2025-05-08","cited_by_count":4,"area":null},{"id":"2410.07395","title":"LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts","created":"2024-10-09","cited_by_count":3,"area":null},{"id":"2503.21155","title":"Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline","created":"2025-03-27","cited_by_count":3,"area":null},{"id":"2508.09263","title":"LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data","created":"2025-08-12","cited_by_count":2,"area":null},{"id":"2502.20268","title":"Large Language Models as Attribution Regularizers for Efficient Model Training","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2505.05744","title":"Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification","created":"2025-05-09","cited_by_count":0,"area":null},{"id":"2508.17735","title":"SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2508.21561","title":"Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2605.24417","title":"Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2606.11640","title":"TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2609.39639","title":"Marginal Response Surface Elicitation for Zero-Label Tabular Learning","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}