Collaborative Large and Small Language Models for Accurate and Scalable Data Repair

2026-06-16

AI Summary

This work addresses the performance degradation in data repair caused by low-quality contextual information and uncertain model outputs. To tackle this challenge, the authors propose LasRepair, a novel framework that synergistically combines a large language model (LLM) as a global context selector with a small language model (SLM) as an efficient repair executor. The framework further incorporates an Expectation-Maximization (EM) algorithm for iterative optimization and a column-wise calibration mechanism with confidence-weighted aggregation. This approach represents the first effort to collaboratively leverage LLMs and SLMs for data repair, substantially enhancing both accuracy and robustness. Experimental results on real-world datasets demonstrate an average F1-score improvement of 18.1% over the strongest baseline, with both theoretical analysis and empirical evaluation confirming the superiority of the proposed method.

Abstract

We study the problem of data repair, a key task in data cleaning that corrects erroneous entries in raw datasets to improve overall data quality. Although recent data-driven methods, especially those based on large language models (LLMs), achieve remarkable performance, we observe that: (i) they directly repair data in the raw and low-quality context, which may compromise learning signals, and (ii) they directly use uncertain model outputs as repairs, potentially introducing unreliable corrections and compromising repair quality. Motivated by the efficiency of small language models (SLMs) and the capabilities of LLMs, and aiming to address the above limitations, we propose LasRepair, a framework that collaborates Large and small language models for data repair. LasRepair employs an LLM as an instructor, which selects a global repair context to guide the SLM. The SLM acts as a corrector, using the selected context to repair erroneous data more efficiently. Moreover, to further improve context quality, we extend LasRepair to LasRepair+, which formulates data repair as an Expectation-Maximisation (EM) procedure that alternates between an E-step for updating the corrector parameters and an M-step for refining the repair context. Furthermore, to mitigate model uncertainty, we propose LasRepair++, which uses column-calibrated model confidence to down-weight unreliable repaired rows when updating the corrector, thereby enhancing repair quality. Theoretical analysis and empirical evaluation demonstrate the superiority of our methods. We theoretically prove the effectiveness of the EM-style procedure and the confidence-based weighting. Experiments on real-world datasets show that LasRepair++~ achieves an average F1-score improvement of 18.1% over the strongest baseline.

Problem

• data repair
• large language models
• small language models
• model uncertainty
• data cleaning

Innovation

• collaborative language models
• data repair
• expectation-maximisation
• model uncertainty calibration
• small and large language models