DsDm: Model-Aware Dataset Selection with Datamodels (2401.12926v1)

Abstract: When selecting data for training large-scale models, standard practice is to filter for examples that match human notions of data quality. Such filtering yields qualitatively clean datapoints that intuitively should improve model behavior. However, in practice the opposite can often happen: we find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. To develop better methods for selecting data, we start by framing dataset selection as an optimization problem that we can directly solve for: given target tasks, a learning algorithm, and candidate data, select the subset that maximizes model performance. This framework thus avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks. Our resulting method greatly improves LLM (LM) performance on both pre-specified tasks and previously unseen tasks. Specifically, choosing target tasks representative of standard LM problems and evaluating on diverse held-out benchmarks, our selected datasets provide a 2x compute multiplier over baseline methods.

• The paper introduces DsDm, a novel dataset selection framework that leverages datamodels to optimize language model performance by reframing data selection as an optimization problem.
• It outperforms traditional methods by achieving a '2 compute multiplier,' with improved results on benchmarks like SQuAD and Jeopardy.
• DsDm enhances broader model generalization by systematically aligning training data with future deployment tasks, promising practical efficiency and resource savings.

Overview of "DsDm: Model-Aware Dataset Selection with Datamodels"

The paper "DsDm: Model-Aware Dataset Selection with Datamodels" by Engstrom, Feldmann, and Madry introduces a novel approach for dataset selection that promises to enhance the training of large-scale LLMs (LMs) by focusing on maximizing model performance rather than merely relying on traditional quality metrics. Through a detailed exploration of datamodels, this research presents a robust framework for training data optimization which diverges from standard methodologies that prioritize data similarity to preselected high-quality sources.

Core Contributions

The authors start by challenging the common practice of selecting training data based on similarity to high-quality datasets, such as Wikipedia, which they argue does not necessarily lead to improved model performance. They offer an innovative alternative by framing dataset selection as an optimization problem aimed at improving model outcomes across various target tasks. This process is realized through the development of "Dataset Selection with Datamodels" (DsDm).

DsDm stands out by explicitly modeling how the learning process utilizes training data subsets to predict on target tasks. The approach is centered on datamodels, which are employed to approximate the relationship between data subset choices and model performance efficiently. This method facilitates selecting data subsets predicted to be most beneficial for enhancing LM tasks.

Evaluation and Results

In a rigorous experimental evaluation, the authors demonstrate the efficacy of DsDm across multiple LM tasks, including SQuAD, LAMBADA, Jeopardy, and CS-Algorithms. Their method consistently outperformed traditional selection methods, which often failed to surpass the performance of randomly selected subsets of the data. Specifically, DsDm provided what they refer to as a "2 compute multiplier," meaning that LMs trained on DsDm-selected datasets performed as if they had been trained with twice the computational resources under traditional random selection methods.

Furthermore, when tasked with improving broader model generalization, DsDm was able to adeptly enhance performance on a wide range of unseen benchmarks by choosing target tasks aligned with anticipated deployment scenarios. This indicates considerable potential for DsDm in real-world applications where model versatility across unknown future tasks is required.

Implications and Future Directions

The implications of this research are twofold. Practically, DsDm has the potential to significantly reduce computational requirements and resource expenditures while maintaining, or even improving, model quality. Theoretically, it provides insights into the importance of the model training process itself in data selection, challenging assumptions that high textual similarity equates to high utility.

Future work could extend to refining datamodel approximations or exploring their applications in diversely structured data environments, including reinforcement learning or complex multi-modal datasets. Additionally, extending this framework beyond LMs into other domains could reveal further efficiencies and improvements.

The paper opens a promising avenue in AI research, suggesting that a deeper understanding and integration of the model training process into dataset selection criteria can yield powerful results, moving the field towards more intelligent and resource-efficient AI development strategies.