An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2007.06778v3)

Abstract: Recent work has shown that pre-trained LLMs such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.

• The paper shows that including counterexamples significantly improves pre-trained models' ability to overcome spurious correlations.
• It demonstrates that larger models with diverse pre-training data reduce reliance on misleading dataset patterns.
• It suggests that multi-task learning enhances model generalization in data-scarce settings without harming in-distribution performance.

An Empirical Study on Robustness to Spurious Correlations using Pre-trained LLMs

The paper "An Empirical Study on Robustness to Spurious Correlations using Pre-trained LLMs" presents an investigation into how pre-trained LLMs, such as BERT, perform regarding spurious correlations observed in datasets. The research specifically addresses the improvement in robustness of these models when exposed to a minority of counterexamples, counteracting the typical reliance on spurious correlations found within natural language processing tasks like natural language inference (NLI) and paraphrase identification (PI).

Key Findings

The core observation of this paper is that pre-trained models exhibit improved performance when faced with datasets designed to challenge their reliance on spurious correlations. This improvement is primarily attributed to the models' ability to leverage a small set of counterexamples from the training data which defy these spurious correlations. The paper identifies that:

1. Dependence on Counterexamples: The robust accuracy of pre-trained models decreases markedly when counterexamples are excluded from the training dataset, highlighting their role in mitigating reliance on spurious correlations.
2. Role of Model Size and Data Diversity: Improved robustness is also associated with larger models, greater amounts of pre-training data, and longer fine-tuning phases. The paper emphasizes the importance of data diversity in pre-training, showing that even low-frequency variations in training sets can positively impact model robustness.
3. Impact of Data Scarcity and Multi-task Learning (MTL): In scenarios where counterexamples are extremely limited, MTL is suggested as a method to enhance the robustness of pre-trained models, underscoring an increase in generalization capabilities from these minority examples without a detrimental effect on in-distribution performance.

Methodology

The research utilizes benchmark datasets such as MultiNLI (MNLI), HANS, PAWS, and PAWS-QQP, which are well-documented in their inclination to promote certain spurious correlations. By conducting multiple experiments with these datasets, the researchers assess the role of pre-trained LLMs in addressing these spurious correlations and the impact of data diversity, model scale, and auxiliary task introduction through MTL.

Implications

The findings have several implications for the field of NLP and beyond:

• Data Diversity and Sparsity: The critical role of data diversity suggests potential pathways for future research, such as designing more effective data collection methodologies and the strategic use of data augmentation to enhance model robustness.
• Pre-training and Fine-Tuning Strategies: The outlined importance of model size and data extent encourages a reevaluation of current strategies used in training robust LLMs, hinting at improvements in training efficiency and performance scalability.
• Generalization and Overfitting Distinctions: The paper propels discussions on the intricacies of generalization versus overfitting in pre-trained models, prompting further investigation into how different pre-trainings influence model robustness.

Future Directions

The paper opens numerous avenues for future exploration. Notably, there is a keen interest in understanding why pre-trained models exhibit considerable resistance to overfitting minority examples and how initialization variabilities from diverse pre-trained models affect overall model performance. Additionally, the paper suggests exploring methodologies for augmenting human-in-the-loop data diversity to enrich dataset relevancy for pre-training large models, thus broadening the scope of robust out-of-distribution generalization efforts.

In summary, this paper provides significant insights into the relationship between pre-trained LLMs and the inherent challenges of spurious correlations in NLP tasks. While highlighting the effectiveness of pre-training and task diversification in enhancing model robustness, it paves the way for more focused research aimed at overcoming current limitations through innovative data handling and training methodologies.