DISCO: Distilling Counterfactuals with Large Language Models
(2212.10534)Abstract
Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsourced, such data is typically limited in scale and diversity; when generated using supervised methods, it is computationally expensive to extend to new counterfactual dimensions. In this work, we introduce DISCO (DIStilled COunterfactual Data), a new method for automatically generating high quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters these generations to distill high-quality counterfactual data. While task-agnostic, we apply our pipeline to the task of natural language inference (NLI) and find that on challenging evaluations such as the NLI stress test, comparatively smaller student models trained with DISCO generated counterfactuals are more robust (6% absolute) and generalize better across distributions (2%) compared to models trained without data augmentation. Furthermore, DISCO augmented models are 10% more consistent between counterfactual pairs on three evaluation sets, demonstrating that DISCO augmentation enables models to more reliably learn causal representations. Our repository is available at: https://github.com/eric11eca/disco
Overview
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DISCO proposes a novel approach utilizing LLMs for generating high-quality, diverse counterfactual data to address biases in NLI dataset, improving robustness and generalization in models.
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The methodology involves a two-stage process: generating diverse perturbations with LLMs, followed by filtering for quality with a task-specific teacher model, demonstrating an efficiency over traditional CAD methods.
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Empirical validations show that DISCO-enhanced models exhibit significant improvements in robustness, generalization across distributions, and counterfactual consistency, with better performance than models trained on human-generated data.
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The study illustrates theoretical and practical implications for advancing AI through scalable, efficient counterfactual reasoning, and suggests future research directions in task and language generalization, prompt engineering, and semi-supervised learning.
Enhancing Robustness and Generalization in NLI with DISCO: DIStilled COunterfactual Data Generation
Introduction to DISCO
The development and training of models in Natural Language Inference (NLI) have encountered significant challenges due to dataset biases. These biases present formidable obstacles for models to achieve robustness and generalize well across different tasks. Traditional counterfactual data augmentation (CAD) methods either involve manual human annotation, which is slow and not easily scalable, or automatic text generation, which lacks diversity and is computationally expensive. Addressing these issues, this paper introduces DISCO (DIStilled COunterfactual Data), a novel approach that utilizes LLMs for generating high-quality, diverse counterfactual data at scale.
Overview of DISCO Methodology
DISCO applies a two-stage process to engineer and distill counterfactual instances. Firstly, it employs prompt engineering with LLMs to generate a diverse set of phrasal perturbations for selected task instances. Secondly, it uses a task-specific teacher model to filter these generations, ensuring only high-quality counterfactual data is kept.
The efficacy of DISCO is demonstrated through its application to the NLI task. The method surpasses traditional CAD methods in generating counterfactual data that is not only more diverse but is also of higher quality, as evidenced by an 83% label-flip success rate on generated instances, which is slightly higher than what is achieved with human annotations.
Empirical Validation and Results
DISCO's effectiveness is rigorously tested through a series of experiments and comparisons with existing augmentation methods. Key findings include:
- Robustness and Generalization: Models trained with DISCO-generated counterfactual data exhibit significant improvements in robustness and generalization capabilities. Specifically, there's a noted 6% increase in robustness and a 2% increase in cross-distribution generalization capabilities compared to models trained without CAD.
- Counterfactual Consistency: Enhanced consistency is observed with a 10% improvement in models' performance across counterfactual pairs in three diverse evaluation sets, underscoring DISCO's ability to enable models to more consistently learn causal representations.
- Manual and Automatic Evaluation of Counterfactual Quality: DISCO-generated data was found to possess superior perturbation diversity and quality, achieving lower Self-BLEU scores and higher OTDD (optimal transport dataset distance) metrics than human-generated counterparts.
Theoretical and Practical Implications
The facts laid out in this research have profound implications for both the theoretical understanding and practical applications of counterfactual reasoning in AI. Theoretically, it advances our understanding of how LLMs can be leveraged for generating diverse counterfactuals efficiently and at scale, an area that remains underexplored. Practically, it presents a scalable method to enhance dataset diversity and quality, which is crucial for developing robust and generalizable AI systems.
Potential Future Directions
The methods and results presented open avenues for future research in several aspects:
- Task and Language Generalization: Extending the DISCO methodology beyond NLI and English to explore its applicability across different tasks and languages.
- Prompt Engineering: Further experiments around prompt engineering could yield insights into optimizing LLMs for counterfactual generation in diverse domains.
- Semi-supervised Learning: Investigating semi-supervised learning techniques to utilize the breadth of data produced by LLMs could enhance the selection process of high-quality counterfactuals.
Conclusion
DISCO introduces a highly promising approach to generating counterfactual data, addressing critical limitations in scalability, diversity, and quality that have hindered previous methods. Its success in improving NLI models on robustness, out-of-domain generalization, and counterfactual reasoning benchmarks underscores its potential to significantly advance the field of artificial intelligence by facilitating the development of more robust and generalizable models.
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