LGAICLDBJul 22

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607.1984720.4Has Code
Predicted impact top 2% in LG · last 90 daysOriginality Incremental advance
AI Analysis

For data cleaning practitioners, Auto-Fill offers a cost-effective, high-precision solution for missing-value prediction in tabular data, addressing overconfidence and hallucination issues of large models.

Auto-Fill uses three specialist small language models (SLMs) for world knowledge, text reasoning, and code reasoning, combined with a calibrated ensemble, to predict missing table values. It outperforms frontier models like o3-pro, Gemini 3 Pro, and DeepSeek R1 on 11 benchmarks with 2200 tables, at less than 1% of their cost.

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.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes