Summary of Sketchfill: Sketch-guided Code Generation For Imputing Derived Missing Values, by Yunfan Zhang et al.
SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing Values
by Yunfan Zhang, Changlun Li, Yuyu Luo, Nan Tang
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Databases (cs.DB); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the long-standing problem of missing value imputation (MVI) in tabular data using large language models (LLMs). They highlight the limitations of existing LLM techniques, such as in-context learning and Chain-of-Thought (CoT), in performing complex reasoning for MVI, particularly when imputing derived missing values. To address this gap, they propose SketchFill, a novel sketch-based method to guide LLMs in generating accurate formulas to impute missing numerical values. The authors demonstrate the effectiveness of SketchFill through experimental results, achieving significant improvements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can use big language models to clean and fix data problems. It shows that these models aren’t very good at doing complex math to fill in missing numbers. To solve this problem, the researchers created a new way called SketchFill that makes it easier for the models to come up with correct answers. They tested SketchFill and found that it worked much better than other methods. |
