DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
Researchers introduce DataPrep-Bench, a unified benchmark to measure the capabilities of large language models (LLMs) in preparing training data end-to-end. The benchmark evaluates two complementary capabilities: data construction and data quality evaluation.
Intelligence analysis by Llama
DataPrep-Bench is the first unified benchmark to jointly evaluate data construction and data quality evaluation under a shared downstream-grounded protocol. It provides a framework for measuring progress on both capabilities as co-equal targets of LLM-driven data preparation.
Imagine you have a big box of LEGOs, and you want to build a specific castle. But the box is full of random LEGO pieces, and you need to sort them out to build the castle. DataPrep-Bench is like a tool that helps you sort out the LEGOs and build the castle. It measures how well large language models (LLMs) can sort out the LEGOs and build the castle, which is important for making the LLMs work well.
Analysis
A Unified Benchmark for LLM-Driven Data Preparation
DataPrep-Bench is a groundbreaking benchmark that evaluates the capabilities of large language models (LLMs) in preparing training data end-to-end. The benchmark introduces a unified framework for measuring progress on both data construction and data quality evaluation, two complementary capabilities that are crucial for the success of LLMs.
Data Construction: Transforming Raw Sources into Supervised Training Data
Data construction is the process of transforming raw sources into supervised training data. This capability is critical for LLMs, as it determines the quality of the training data that the models receive. DataPrep-Bench evaluates data construction methods by scoring them on their ability to fine-tune a base model on their outputs jointly with Dolly-15k.
Data Quality Evaluation: Predicting the Training Value of Candidate Datasets
Data quality evaluation is the process of predicting the training value of candidate datasets before downstream training. This capability is essential for LLMs, as it helps to ensure that the models receive high-quality training data. DataPrep-Bench evaluates data quality evaluation methods by scoring them on their ability to predict the training value of candidate datasets using Pearson correlation with downstream performance.
The Distributional Alignment Score (DAS): A Distribution-Based Evaluator
DataPrep-Bench introduces the Distributional Alignment Score (DAS), a distribution-based evaluator that uses Maximum Mean Discrepancy (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.
Conclusion
DataPrep-Bench provides a unified framework for measuring progress on both data construction and data quality evaluation. The benchmark introduces a new standard for evaluating LLM-driven data preparation and provides a valuable resource for researchers and practitioners working on LLMs.
Key points
- DataPrep-Bench is a unified benchmark for evaluating LLM-driven data preparation.
- The benchmark evaluates two complementary capabilities: data construction and data quality evaluation.
- DataPrep-Bench introduces a new standard for evaluating LLM-driven data preparation.
- The Distributional Alignment Score (DAS) is 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.
If DataPrep-Bench becomes a widely adopted standard, it could lead to significant improvements in the quality of training data for LLMs, which in turn could lead to better performance and more accurate results from these models.
If DataPrep-Bench is not widely adopted, it could lead to a lack of standardization in the evaluation of LLM-driven data preparation, which could hinder progress in this area and make it harder to compare the performance of different LLMs.
