Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond (2402.17327v1)

Abstract: We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on $k$-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data with respect to which the model loss is H\"older continuous, our approach provably allows selecting a set of ``typical'' $k + 1/\varepsilon^2$ elements whose average loss corresponds to the average loss of the whole dataset, up to a multiplicative $(1\pm\varepsilon)$ factor and an additive $\varepsilon \lambda \Phi_k$, where $\Phi_k$ represents the $k$-means cost for the input embeddings and $\lambda$ is the H\"older constant. We furthermore demonstrate the performance and scalability of our approach on fine-tuning foundation models and show that it outperforms state-of-the-art methods. We also show how it can be applied on linear regression, leading to a new sampling strategy that surprisingly matches the performances of leverage score sampling, while being conceptually simpler and more scalable.

Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond

The paper "Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond" addresses the challenge of training machine learning models with large datasets efficiently by introducing a novel data selection method. This approach leverages $k$-means clustering and sensitivity sampling to enable the selection of smaller representative subsets of data, facilitating the training of machine learning models in a resource-efficient manner.

Fundamental Propositions

The authors explore the concept of data selection where the goal is to determine a succinct subset of the data that can faithfully represent the entire dataset during model training. The paper proposes a methodology that intertwines $k$-means clustering and sensitivity sampling, with theoretical backing, ensuring the selection of a set of typical elements from the dataset. The loss for this subset mirrors the overall dataset's average loss within a controllable error margin defined by an additive term influenced by the $k$-means clustering cost and a multiplicative factor.

The authors assert that their method, when applied to fine-tuning foundation models, surpasses contemporary strategies both in performance and scalability. They demonstrate that this approach can also be extended to linear regression tasks with results rivalling leverage score sampling methods, while presenting conceptual simplicity and better scalability.

Theoretical Analysis and Algorithmic Approach

The paper introduces an innovative algorithmic framework through the concept of H\"older continuity for model loss, a less stringent assumption than Lipschitz continuity applied by prior research. The theoretical foundation of this work suggests that the approach is robust across various applications, including foundation models, supporting the generalization of this data-selection methodology.

Specifically, the authors contribute the following advancements:

1. A systematic approach that allows for more robust selection of data points mitigating issues associated with outliers.
2. Demonstration of strong theoretical results with minimal assumptions on dataset embeddings, allowing broader applicability across tasks.
3. Empirical evidence showcasing the method's efficacy in comparison to traditional methods on benchmark datasets such as MNIST and for fine-tuning LLMs.

Numerical Results and Practical Implications

The empirical results are robust, highlighting the practicality of this method in scenarios demanding efficiency. For instance, results reveal a noticeable improvement in fine-tuning a T5-Small model for machine translation tasks, using representative data subsets. Accuracy improvements are highlighted against the baseline methods such as random sampling and state-of-the-art data selection approaches.

The paper's methodology, when applied to linear regression, provides competitive outcomes to advanced techniques such as leverage score sampling. This signals a significant contribution to the active learning domain, suggesting that clustering-based sensitivity-driven sampling could become a standard procedure for data-efficient learning in varied contexts.

Future Directions

Given the promising results, further investigation could be directed towards extending this approach to a broader range of machine learning settings, exploring hyperparameter optimization techniques in the clustering process, or evaluating this method within the field of unsupervised or semi-supervised learning frameworks. Additionally, integrating this data selection methodology with more sophisticated models could yield insights into the scalability limits and further efficiencies.

In summary, the paper effectively bridges theoretical underpinnings and practical implications, empowering data-efficient model training through clustering-based approaches. The proposed method offers a valuable trajectory towards optimized resource usage without compromising the performance of machine learning models, marking a significant step in the quest for practical and efficient AI solutions.