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

• URL: http://arxiv.org/abs/2508.09263v1
• Date: Tue, 12 Aug 2025 18:07:11 GMT
• Title: LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
• Authors: Peng Wang, Dongsheng Wang, He Zhao, Hangting Ye, Dandan Guo, Yi Chang,
• Abstract summary: Large language models (LLMs) have opened the door to in-depth investigation of their potential in data modeling.<n>We propose a novel LLM-based prototype estimation framework for tabular learning.<n>Our key idea is to query the LLM to generate feature values based example-free prompt.<n>Ours bypasses the constraints brought by the example-based prompt, providing a scalable and robust framework.
• Score: 18.260760417447084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent breakthroughs in large language models (LLMs) have opened the door to in-depth investigation of their potential in tabular data modeling. However, effectively utilizing advanced LLMs in few-shot and even zero-shot scenarios is still challenging. To this end, we propose a novel LLM-based prototype estimation framework for tabular learning. Our key idea is to query the LLM to generate feature values based example-free prompt, which solely relies on task and feature descriptions. With the feature values generated by LLM, we can build a zero-shot prototype in a training-free manner, which can be further enhanced by fusing few-shot samples, avoiding training a classifier or finetuning the LLMs. Thanks to the example-free prompt and prototype estimation, ours bypasses the constraints brought by the example-based prompt, providing a scalable and robust framework. Extensive experiments demonstrate the effectiveness of ours in zero and few-shot tabular learning.

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