Efficient Lifelong Model Evaluation in an Era of Rapid Progress (2402.19472v2)

Abstract: Standardized benchmarks drive progress in machine learning. However, with repeated testing, the risk of overfitting grows as algorithms over-exploit benchmark idiosyncrasies. In our work, we seek to mitigate this challenge by compiling ever-expanding large-scale benchmarks called Lifelong Benchmarks. These benchmarks introduce a major challenge: the high cost of evaluating a growing number of models across very large sample sets. To address this challenge, we introduce an efficient framework for model evaluation, Sort & Search (S&S)}, which reuses previously evaluated models by leveraging dynamic programming algorithms to selectively rank and sub-select test samples. To test our approach at scale, we create Lifelong-CIFAR10 and Lifelong-ImageNet, containing 1.69M and 1.98M test samples for classification. Extensive empirical evaluations across over 31,000 models demonstrate that S&S achieves highly-efficient approximate accuracy measurement, reducing compute cost from 180 GPU days to 5 GPU hours (about 1000x reduction) on a single A100 GPU, with low approximation error and memory cost of <100MB. Our work also highlights issues with current accuracy prediction metrics, suggesting a need to move towards sample-level evaluation metrics. We hope to guide future research by showing our method's bottleneck lies primarily in generalizing Sort beyond a single rank order and not in improving Search.

• The paper presents a dynamic lifelong benchmark framework that combats overfitting by continuously expanding diverse test datasets.
• It introduces the S{content}S method, which reduces computation from 180 GPU days to 5 GPU hours, achieving remarkable efficiency gains.
• Empirical validation on ~31,000 models shows high accuracy correlations, underscoring the method’s potential for scalable, reliable evaluation.

Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress

The paper "Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress" by Prabhu et al. addresses significant challenges in the field of machine learning evaluations, where static benchmarks such as ImageNet and CIFAR-10 have traditionally dominated. As models are repeatedly tested on these static datasets, there exists an inherent risk of overfitting to the peculiarities of the dataset rather than genuinely learning to generalize. This research proposes using "Lifelong Benchmarks," a dynamic alternative designed to persistently expand and mitigate the overfitting issue by compiling ever-growing pools of test samples, effectively setting a new paradigm for model evaluation.

Methodological Contributions

Lifelong Benchmarks Construction: The authors introduce Lifelong-CIFAR10 and Lifelong-ImageNet benchmarks. These benchmarks are not static but designed to evolve by including a diverse array of samples that cumulatively exceed 1.6 million in size, extracted from a range of domains to maintain global distributional characteristics. In Lifelong-CIFAR10, 1.69 million test samples are selected, while for Lifelong-ImageNet, the count goes up to 1.98 million samples. This endeavor ensures that the benchmarks represent a wide-cross-section of visual domains and are thus more resistant to overfitting.

Efficient Model Evaluation Framework: To address the pressing challenge of evaluation cost exacerbated by continually expanding benchmark sizes, the authors propose an efficient evaluation methodology titled "Sort {content} Search" (S{content}S). Drawing inspiration from Computerized Adaptive Testing (CAT), S{content}S optimizes benchmarking evaluations by employing dynamic programming to estimate model performance using significantly fewer samples. This method reduces computational overhead from 180 GPU days to merely 5 GPU hours on a single A100 GPU, indicating a remarkable 1000x efficiency gain.

Experimental Results and Analysis

The proposed benchmarks and framework were empirically validated using $\sim$31,000 models, delivering promising results in balancing evaluation accuracy and cost. The Sort {content} Search algorithm demonstrated low approximation error, evidencing its utility in highly-efficient approximate accuracy estimation. Error decomposition analysis in the paper confirmed that much of the error was irreducible, tied not to sampling inefficiencies but inherent duality in sample ordering and model suitability. Despite significant reductions in compute cost, the model performances predicted were highly consistent with ground truths, showcased by high correlation coefficients between estimated and actual accuracies.

Implications and Future Directions

The implications of this research are profound, both practically and theoretically. Practically, it provides a scalable solution to the "benchmark exhaustion" problem, enabling continuous evaluation without an overwhelming resource burden. Theoretically, it suggests a shift in benchmark design philosophy, aligning it more with the use case realities and expansive, diverse datasets that better simulate real-world scenarios.

Looking forward, some promising research avenues emerge from this work. (1) Extending the current one-step evaluation process to a multi-step one may capture a wider range of model behaviors. (2) Given that the systematic ranking induced epistemic errors were largely irreducible, forthcoming research could explore non-linear sample ranking structures to better accommodate diverse model characteristics. Finally, (3) automating hard sample discovery and labelling can further enhance the robustness of Lifelong Benchmarks, ensuring they align closely with evolving models and their complex failures.

In conclusion, this paper sets a clear trajectory away from traditional, static benchmarks towards more agile, representative, and efficient systems of evaluation, capturing the rapid evolution of models and the diversity inherent in the data with which these models interact. As AI continues to pervade various domains, such methodological innovations are critical for ensuring integrity, reliability, and generalizability in machine learning systems.