Adversarial Filters of Dataset Biases (2002.04108v3)

Abstract: Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples. This raises the question of whether these models have learned to solve a dataset rather than the underlying task by overfitting to spurious dataset biases. We investigate one recently proposed approach, AFLite, which adversarially filters such dataset biases, as a means to mitigate the prevalent overestimation of machine performance. We provide a theoretical understanding for AFLite, by situating it in the generalized framework for optimum bias reduction. We present extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks. Finally, filtering results in a large drop in model performance (e.g., from 92% to 62% for SNLI), while human performance still remains high. Our work thus shows that such filtered datasets can pose new research challenges for robust generalization by serving as upgraded benchmarks.

• The paper introduces AFLite, a lightweight adversarial filtering method that iteratively removes predictable data instances to mitigate bias.
• The paper validates AFLite through experiments across synthetic data, NLP, and image classification, showing significant drops in biased performance metrics.
• The paper establishes a formal framework for bias reduction, offering actionable insights for developing more robust and generalized AI models.

Adversarial Filters of Dataset Biases: A Structured Analysis

The paper "Adversarial Filters of Dataset Biases" addresses the pervasive issue of dataset biases that compromise the generalization ability of large neural models. These biases often lead to a significant performance gap between in-distribution evaluations and adversarial or out-of-distribution testing scenarios.

Overview

The primary focus of the work is a thorough investigation of the AFLite method, which stands for Lightweight Adversarial Filtering. AFLite is proposed as a general mechanism to filter out spurious dataset biases, thereby improving models' ability to generalize beyond the specific datasets they are trained on. The paper situates AFLite within a theoretical framework aimed at minimizing representation bias in datasets by iteratively removing predictable instances that artificially inflate model performance.

Theoretical Framework

The paper presents a formal framework for understanding dataset bias and the representational predictability that arises from spurious correlations within data samples. AFLite is positioned as a heuristic approximation of an optimal, yet intractable, bias reduction strategy. The framework defines a predictability score for data instances, which helps in identifying and eliminating biased examples from the dataset iteratively.

Experimental Analysis

Experiments are conducted across synthetic datasets, diverse NLP tasks, and image classification to validate the effectiveness of AFLite. The approach is shown to reduce the influence of dataset biases significantly, facilitating improved generalization, particularly in adversarial and out-of-distribution contexts.

1. Synthetic Data: Through synthetic experiments, AFLite effectively removes instances with artificial biases, making linear classification models struggle post-filtering—indicating successful bias reduction.
2. NLP: On the SNLI benchmark, training models on AFLite-filtered data improved zero-shot performance across multiple NLI diagnostic datasets, including HANS and Adversarial NLI, while revealing inflated performance metrics on the original datasets. This underscores AFLite’s ability to produce more challenging and realistic evaluation benchmarks.
3. Image Classification: For ImageNet, filtering using AFLite led to reduced performance on the ImageNet validation set, reflecting its potential for generating robust benchmarks. Moreover, filtered datasets improved agreement with ImageNet-A adversarial settings, highlighting AFLite’s utility in enhancing model generalization.

Numerical Results

The paper provides significant numerical evidence, such as the drop in SNLI accuracy from 92% to 62% after applying AFLite, advocating for a more honest appraisal of model capabilities. ImageNet classification accuracy also showed notable declines post-filtering, emphasizing the robustness of the filtered datasets as a new benchmark.

Implications and Speculative Insights

The introduction of AFLite indicates a step forward in understanding and mitigating the problems posed by dataset biases in machine learning models. Practically, it offers a pathway to develop more resilient AI systems capable of better generalization. Theoretically, it opens avenues for future research into dataset bias management, potentially informing data collection and curation strategies.

Future research could explore further optimizations of AFLite and its applicability to other domains beyond NLP and computer vision. As AI models grow in complexity and scope, tools like AFLite can play critical roles in ensuring these models are not just powerful but also broadly applicable across varying real-world scenarios.

In summary, the paper provides a compelling and empirically validated framework for addressing biases in datasets, forming an essential component for the advancement of more generalizable AI models.