Data Shapley in One Training Run (2406.11011v2)

Abstract: Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. However, existing approaches require re-training models on different data subsets, which is computationally intensive, foreclosing their application to large-scale models. Furthermore, they produce the same attribution score for any models produced by running the learning algorithm, meaning they cannot perform targeted attribution towards a specific model obtained from a single run of the algorithm. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest. In its most efficient implementation, our technique incurs negligible additional runtime compared to standard model training. This dramatic efficiency improvement makes it possible to perform data attribution for the foundation model pretraining stage for the first time. We present several case studies that offer fresh insights into pretraining data's contribution and discuss their implications for copyright in generative AI and pretraining data curation.

An Expert Analysis of the "Data Shapley in One Training Run" Study

In the constantly evolving field of machine learning, the notion of differentiating and quantifying the contribution of individual data points to training a model has significant implications for both legal and practical applications. The paper "Data Shapley in One Training Run" offers a fresh perspective by addressing the computational inefficiencies traditionally associated with Data Shapley values and introducing the concept of In-Run Data Shapley. This approach not only aligns with the scalability requirements for large-scale machine learning models but also seamlessly integrates into the training process of modern deep learning algorithms.

Core Contributions

The primary goal of Data Shapley is to provide a fair evaluation of how each data point contributes to the final trained model. Traditional methods necessitate extensive computational resources as they involve retraining models on various subsets of data, which is neither feasible nor efficient at the scale required for foundation models like GPT-2. The authors present In-Run Data Shapley, a methodology that allows for efficient attribution of data during a single training cycle. This necessitates minimal additional runtime compared with standard training, a critical advantage given the computational demands of machine learning at scale.

This innovation is grounded in the first and second-order Taylor expansions that enable the analytic derivation of Shapley values as approximations based on gradient dot-products and gradient-Hessian-gradient products. Thus, data valuation becomes tractable even for foundation models such as GPT-2. The introduction of "ghost" techniques further optimizes computation, effectively reducing the per-sample gradient calculations required by traditional methods.

Numerical and Empirical Highlights

This paper substantiates its claims through rigorous empirical evaluations using the GPT2 model trained on the uncopyrighted Pile dataset. Noteworthy findings include a significant reduction in the computational burden by more than 30 times compared to naive approaches. Moreover, the research demonstrates the applicability of In-Run Data Shapley in diverse scenarios such as data curation, revealing that even well-curated datasets contain a considerable proportion of data with negative contribution to the training process.

The analysis shows superior relevance detection capabilities when compared to benchmark techniques like the influence function. Precisely, scenarios where paraphrased or semantically similar validation datasets still find significant attribution, underscore the broader implications of fairness and copyright concerns, as even abstract associations with the data can contribute valuably.

Theoretical and Practical Implications

From a theoretical standpoint, In-Run Data Shapley promotes a shift from an average contribution model to one that is more specific and relevant to the exact model iteration and data sequence used in training. This specificity could lead to more robust interpretations and better alignment with real-world scenarios, where data is rarely analyzed in isolation but instead interacts as part of a larger system.

Practically, this method provides data scientists and legal entities with a tool that could address data-driven copyright disputes and optimize dataset curation, enhancing the quality of the resulting ML models. Moreover, it stands to impact the compensation models for data providers based on precise contribution analyses.

Future Prospects

While this research marks a significant advancement in data attribution within machine learning, there are pathways yet to explore. For instance, extending these techniques to incorporate adaptive optimizers like Adam would provide broader applicability. Furthermore, integrating these methods in real-time learning scenarios, such as federated learning systems, could vastly broaden the scope of this work.

In conclusion, the introduction of In-Run Data Shapley represents an imperative step forward in the fair and efficient valuation of training data. It aligns perfectly with the current needs for computational efficiency and model-specific interpretability. This contribution holds the potential to transform how data is valued in machine learning, potentially reshaping the legal landscape concerning data usage.