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This paper introduces Auto-Fill, an innovative approach for predicting missing values in tabular data by leveraging three specialist language models (SLMs) tailored for world knowledge, text-based reasoning, and code-based reasoning. The method employs a calibrated ensemble mechanism that dynamically selects the most confident model or abstains from making a prediction, significantly enhancing accuracy while maintaining low operational costs. Extensive evaluations across 11 benchmarks demonstrate that Auto-Fill outperforms leading reasoning models, achieving superior accuracy at less than 1% of their deployment costs.
Achieving high-precision missing-value predictions in tabular data is now possible with a fraction of the cost of traditional models, thanks to the power of specialized language models.
Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in tables, by reasoning holistically across rows and columns, they are costly to deploy at scale and tend to be overconfident, often generating hallucinated or false-positive predictions. In this paper, we observe that achieving high-precision missing-value prediction in tables requires a distinct combination of three capabilities: (1) world knowledge, (2) text-based reasoning, and (3) code-based reasoning. We systematically explore design choices for combining these capabilities, and propose an Auto-Fill approach that post-trains three specialist small language models (SLMs), each optimized for one capability. We develop a calibrated ensemble mechanism that either dynamically selects the most confident specialist or abstains, ensuring high accuracy. Extensive experiments on 11 benchmarks with 2200 real tables drawn from diverse domains show that Auto-Fill achieves superior accuracy compared to state-of-the-art reasoning models (e.g., o3-pro, Gemini 3 Pro, and DeepSeek R1), while operating at a fraction (less than 1%) of the cost of these frontier models. Our results highlight the effectiveness of specialization and calibrated abstention in the important domain of tabular data. Auto-Fill is publicly available at https://github.com/lyrain2001/auto-fill.
Microsoft Research