三个专精小模型组队填表格空值，比大模型还准，成本只要1%——这篇论文值得看。
Auto-Fill提出后训练三个专业小语言模型（SLM），分别专精世界知识、文本推理和代码推理，并通过校准集成机制动态选择最自信的专家或弃权。在11个基准数据集、2200个真实表格上的实验显示，Auto-Fill的准确率超过了o3-pro、Gemini 3 Pro和DeepSeek R1等前沿推理模型。其推理成本不到这些模型的1%。Auto-Fill已开源（GitHub）。
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist 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.