End-to-End Bias Mitigation by Modelling Biases in Corpora

Analysis

Why This Paper Matters

This paper addresses a fundamental problem in natural language understanding: models often exploit superficial dataset biases rather than learning the underlying task, leading to poor generalization on out-of-domain data. The authors propose two end-to-end learning strategies that leverage bias-only models to dynamically adjust training, reducing reliance on spurious correlations. This is significant because it offers a practical, scalable approach to improve model robustness without requiring additional data or architectural changes.

The work is particularly relevant for AI safety and alignment, as biased models can produce unreliable predictions in real-world applications such as fact verification and natural language inference. By focusing on hard examples and down-weighting biased ones, the method directly targets the root cause of poor generalization.

Technical Contributions

• Bias-Only Models: The authors define biases using one or more simple models that learn to exploit known dataset biases (e.g., lexical overlap in NLI). These models serve as proxies for the unwanted correlations.
• Loss Adjustment: During training, the bias-only models' predictions are used to reweight the base model's loss. Specifically, examples where the bias model is confident are down-weighted, forcing the base model to learn from harder, more informative examples.
• Two Strategies: The paper presents two variants: one that uses a single bias model and another that ensembles multiple bias models, allowing flexibility in capturing different types of biases.
• End-to-End Training: The entire pipeline is trained jointly, making it easy to integrate into existing training workflows without separate pre-processing steps.

Results

The authors evaluate on large-scale NLI (SNLI, MNLI) and fact verification (FEVER) benchmarks, testing on out-of-domain datasets designed to assess robustness (e.g., HANS for NLI). The debiasing methods significantly improve accuracy on these challenging sets, with gains of up to 10-15% over standard training. Transfer learning experiments show that models trained with the proposed strategies generalize better to other textual entailment datasets, demonstrating the effectiveness of the approach.

Significance

This work contributes a practical, end-to-end framework for bias mitigation that can be applied to various NLU tasks. By making models more robust to dataset biases, it helps bridge the gap between benchmark performance and real-world reliability. The public release of code and data facilitates further research and adoption. The approach is complementary to other debiasing techniques and can be combined with data augmentation or adversarial training for even stronger robustness.