LDI: Localized Data Imputation for Text-Rich Tables

TL;DR

LDI introduces a localized reasoning framework using LLMs for imputing missing values in text-rich tables, enhancing accuracy, scalability, and interpretability over existing methods.

Contribution

The paper presents a novel localized reasoning approach with targeted attribute and tuple selection, improving imputation accuracy and transparency in text-rich tables.

Findings

LDI achieves up to 8% higher accuracy than state-of-the-art methods.

Localized reasoning reduces noise and improves scalability.

LDI offers transparent attribution for each imputed value.

Abstract

Missing values are pervasive in real-world tabular data and can significantly impair downstream analysis. Imputing them is especially challenging in text-rich tables, where dependencies are implicit, complex, and dispersed across long textual fields. Recent work has explored using Large Language Models (LLMs) for data imputation, yet existing approaches typically process entire tables or loosely related contexts, which can compromise accuracy, scalability, and explainability. We introduce LDI, a novel framework that leverages LLMs through localized reasoning, selecting a compact, contextually relevant subset of attributes and tuples for each missing value. This targeted selection reduces noise, improves scalability, and provides transparent attribution by revealing the dependency relations that justify each selected attribute and the evidence behind each retrieved tuple. It makes clear not only which data influenced a prediction, but also why it was chosen. Through extensive experiments on real and synthetic datasets, we demonstrate that LDI consistently outperforms state-of-the-art imputation methods, achieving up to 8% higher accuracy with hosted LLMs and even greater gains with small local models. The improved interpretability and robustness also make LDI well-suited for high-stakes data management applications. Our code and datasets are publicly available at https://github.com/soroushomidvar/LDI.