Weak-to-Strong Generalization Through the Data-Centric Lens (2412.03881v2)

Abstract: The weak-to-strong generalization phenomenon is the driver for important machine learning applications including highly data-efficient learning and, most recently, performing superalignment. While decades of research have resulted in numerous algorithms that produce strong empirical performance, understanding what aspects of data enable weak-to-strong generalization has been understudied. We propose a simple data-centric mechanism that characterizes weak-to-strong generalization: the overlap density. Intuitively, generalization tracks the number of points that contain overlaps, i.e., both easy patterns (learnable by a weak model) and challenging patterns (only learnable by a stronger model), as with such points, weak predictions can be used to learn challenging patterns by stronger models. We provide a practical overlap detection algorithm to find such points in datasets and leverage them to learn, among multiple sources of data, which to query when seeking to maximize overlap density and thereby enhance weak-to-strong generalization. We present a theoretical result showing that the generalization benefit is a function of the overlap density and a regret bound for our data selection algorithm. Empirically, we validate the mechanism and the overlap detection algorithm on a wide array of settings.

• The paper introduces the novel overlap density metric to quantify mixed easy and hard patterns for enhanced model generalization.
• The paper proposes an algorithm using an Upper Confidence Bound strategy to identify optimal overlap points for targeted data selection.
• The paper empirically demonstrates that leveraging overlap density significantly boosts weak-to-strong generalization across diverse datasets and applications.

Analysis of Weak-to-Strong Generalization via the Data-Centric Lens

The paper "Weak-to-Strong Generalization Through the Data-Centric Lens" by Changho Shin, John Cooper, and Frederic Sala from the Department of Computer Science at the University of Wisconsin-Madison addresses an intriguing aspect of machine learning, specifically the weak-to-strong generalization phenomenon. This concept involves increasing the capabilities of a weak learning model by leveraging information that a stronger model can potentially utilize, enhancing the former's learning process.

Key Contributions and Findings

The paper's principal contribution is the identification of "overlap density," a metric that quantifies the presence of both easy and hard patterns within the data. The overlap density concept is pivotal in understanding how weak models can be augmented to learn from data points that exhibit overlapping patterns. This idea captures a latent characteristic where weak models predict using easy patterns while strong models utilize these predictions to infer and learn more challenging patterns within the data.

The authors propose a novel algorithm aiming to detect overlap points in datasets and illustrate how identifying these points can guide data acquisition from multiple sources to maximize the overlap density. This approach is demonstrated to enhance weak-to-strong generalization empirically across various datasets and in two application areas: fine-tuning scenarios using LLMs and weak supervision settings.

Theoretical and Practical Implications

Theoretically, the authors extend existing work on generalization by building on the framework by Lang et al. (2024), deriving insights critical to the understanding of how overlap density facilitates generalization. This theoretical grounding suggests a need to shift the focus from mere algorithmic improvements in machine learning to a more nuanced examination of the data itself, specifically looking for overlap-rich data that might provide greater generalization benefits when used in training.

On a practical level, the proposed overlap detection and data selection algorithms show promise for applications where data acquisition is guided by the nuances of pattern overlap rather than quantity alone. This has potential benefits in reducing costs associated with data labeling by focusing on sourcing datasets that will most likely reveal the advantageous overlap points.

Observations and Results

The paper's empirical studies robustly validate the proposed overlap density mechanism in multiple settings with both synthetic and real-world data. The experiments consistently reveal that boosting overlap density generally results in noticeable improvements in the performance of a weak-to-strong model. For instance, in controlled experiments with data drawn from a mixture of Gaussians, weak-to-strong models overwhelmingly benefited from training on overlap data as opposed to easy or hard data alone.

Additionally, the use of an Upper Confidence Bound (UCB) algorithm for data source selection underscores an intelligent mechanism through which practitioners can prioritize data acquisition. This algorithm was proven effective in maximizing overlap density, enhancing the generalization ability of weak models.

Future Directions

The research opens several future research avenues, including exploring more complex patterns that involve varying levels of overlapping difficulties, refining the theoretical frameworks related to weak-to-strong generalization, and evolving the overlap identification procedure to accommodate a broader range of real-world datasets and learning contexts.

Conclusion

This work marks a significant step towards understanding and exploiting weak-to-strong generalization from a data-centric perspective. The insights and methodologies presented have the potential to lead to innovations in both research and application domains within machine learning, emphasizing a strategic shift toward smarter, data-driven model training approaches. Such an evolution in approach could prove critical as AI systems continue to advance, demanding more efficient and effective learning paradigms.