CatDB: Data-Catalog-Guided, LLM-Based Generation of Data-Centric ML Pipelines
Bibliographic record
Abstract
Data-centric machine learning (ML) pipelines extend traditional ML pipelines—of feature transformations, hyper-parameter tuning, and model training—by additional pre-processing steps for data cleaning, data augmentation, and feature engineering to create high-quality data with good coverage. Finding effective data-centric ML pipelines is still a labor- and compute-intensive process though. While AutoML tools use effective search strategies, they struggle to scale with large datasets. Large language models (LLMs) show promise for code generation but face challenges in generating data-centric ML pipelines due to private datasets not seen during training, complex pre-processing requirements, and the need for mitigating hallucinations. These demands exceed typical code generation as it requires actions tailored to the characteristics and requirements of a particular dataset. This paper introduces CatDB, a comprehensive, LLM-based system for generating effective, error-free, and efficient data-centric ML pipelines. CatDB leverages data catalog information and refined metadata to dynamically create dataset-specific rules (instructions) to guide the LLM. Moreover, CatDB includes a robust mechanism for automatic validation and error handling of the generated pipeline. Our experimental results show that CatDB reliably generates effective ML pipelines across diverse datasets, achieving accuracy comparable to or better than existing LLM-based systems, standalone AutoML tools, and combined workflows of data cleaning and AutoML tools, while delivering up to orders of magnitude faster performance on large datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".