DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
DataPrep-Bench is the first unified benchmark that jointly evaluates LLMs on data construction and data quality evaluation across six domains and multiple base models. It introduces Data-Construction-Skill agent and Distributional Alignment Score (DAS), achieving state-of-the-art results in several domains.
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[Submitted on 19 May 2026]
Title:DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
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Abstract:The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end. We view LLM-driven data preparation as comprising two complementary capabilities: data construction, which transforms raw sources into supervised training data, and data quality evaluation, which predicts the training value of candidate datasets before downstream training; throughout, "quality" refers to downstream training utility rather than surface-level textual properties. We introduce DataPrep-Bench, the first unified benchmark that jointly evaluates both capabilities under a shared downstream-grounded protocol over six domains and multiple base models. For data construction, methods consume identical raw sources and are scored by fine-tuning a base model on their outputs jointly with Dolly-15k; alongside this track we release Data-Construction-Skill, a skill-guided agent that lifts the Dolly-only baseline by nearly 20 points absolute on Llama-3.1-8B Finance and is competitive with the strongest agent- and DataFlow-based methods in knowledge-extraction-dense domains. For data quality evaluation, scoring functions are scored by Pearson correlation with downstream performance on a shared candidate pool; we release the Distributional Alignment Score (DAS), a distribution-based evaluator that uses MMD between a candidate dataset and a domain proxy. DAS attains the strongest cross-model correlation in four of six domains and is the only metric clearing r > 0.70 simultaneously in Math, Science, and Medical, outperforming existing quality-, diversity-, and heuristic-based evaluators. DataPrep-Bench provides a unified, downstream-grounded framework for measuring progress on both capabilities as co-equal targets of LLM-driven data preparation.
Subjects:
Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2607.20465 [cs.LG]
(or arXiv:2607.20465v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.20465
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hao Liang [view email] [v1] Tue, 19 May 2026 02:23:53 UTC (4,538 KB)
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