Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification

2025-08-29

AI Summary

To address the performance instability of large language models (LLMs) in few-shot tabular classification—stemming from the inherent variability of structured data—this paper proposes a data-driven insight distillation framework. Methodologically, it innovatively integrates three principles: “divide-and-conquer,” “ease-first prioritization,” and “reflective learning,” forming a three-stage distillation pipeline—summarization, exemplar selection, and reflection—that tightly couples LLMs with traditional data modeling techniques (e.g., rule induction and strategic example selection). This synergy enhances robustness to sparse annotations and improves generalization across diverse tabular domains. Empirically, the approach consistently outperforms state-of-the-art methods on nine benchmark datasets. Ablation studies confirm the efficacy of each component, while analytical results demonstrate its capacity to efficiently leverage limited labeled data and mitigate annotation bias.

Technology Category

• Machine Learning: Structured Learning
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent studies show the promise of large language models (LLMs) for few-shot tabular classification but highlight challenges due to the variability in structured data. To address this, we propose distilling data into actionable insights to enable robust and effective classification by LLMs. Drawing inspiration from human learning processes, we introduce InsightTab, an insight distillation framework guided by principles of divide-and-conquer, easy-first, and reflective learning. Our approach integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques. The obtained insights enable LLMs to better align their general knowledge and capabilities with the particular requirements of specific tabular tasks. We extensively evaluate InsightTab on nine datasets. The results demonstrate consistent improvement over state-of-the-art methods. Ablation studies further validate the principle-guided distillation process, while analyses emphasize InsightTab's effectiveness in leveraging labeled data and managing bias.

Problem

• Addresses variability in structured data for few-shot tabular classification
• Proposes distilling data into actionable insights for LLM classification
• Enables LLMs to align general knowledge with specific tabular tasks

Innovation

• InsightTab framework distills data into insights
• Combines LLMs with data modeling techniques
• Uses rule summarization exemplification reflection processes