Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models (2101.00288v2)

Abstract: While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.

• The paper introduces Polyjuice, a framework that generates diverse counterfactuals using fine-tuned transformers to explain and evaluate language models.
• The methodology employs control codes to target specific perturbation types, significantly reducing annotation efforts by 70% compared to manual methods.
• The generated counterfactuals improve model robustness, offer detailed error analysis, and uncover biases beyond traditional evaluation metrics.

Overview of the Polyjuice Approach for Counterfactual Generation

The paper "Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models" explores the development of a general-purpose counterfactual generator leveraging fine-tuned transformer models. The authors propose Polyjuice, a framework for generating diverse and realistic counterfactuals, used in evaluating, explaining, and refining LLMs. This methodology diverges from previous approaches that rely on labor-intensive manual creation or limited automated perturbations, such as word substitutions or paraphrasing.

Central to Polyjuice is its control over the types and locations of perturbations, achieved by fine-tuning GPT-2 on datasets with paired sentences. The paper asserts that Polyjuice produces counterfactuals conducive to multiple applications with significantly reduced annotation efforts (about 70% less), enhancing training, evaluation, model explanation, and error analysis.

Key Contributions and Methods

1. Formalization and Implementation: The authors formalize the task of counterfactual generation to separate generation from specific applications. By conditioning text generation using models like GPT-2, they introduce rich control codes that define the perturbation types (e.g., negation, lexical, quantifier, etc.) and exploit fill-in-the-blank structures to direct specific sentence alterations.
2. Diverse Counterfactual Generation: Polyjuice generates a diverse set of counterfactuals that enable a range of real-world NLP tasks. The model, trained on various datasets, achieves fluency and diversity in its generated outputs and utilizes control mechanisms to provide counterfactuals that are more comprehensive than standard LLMs.
3. Evaluation Through Contrast Sets: By generating contrast sets with counterfactual examples labeled differently from their originals, the paper demonstrates how classifier performance can expose vulnerabilities and biases not apparent in typical evaluations.
4. Model Augmentation: When incorporated into training regimes (e.g., in sentiment analysis and natural language inference), Polyjuice counterfactuals improve model generalization capabilities. Models trained with such modified datasets demonstrate increased robustness, particularly in out-of-domain applications.
5. Explanations and Error Analysis: The paper highlights the utility of counterfactuals in providing detailed model explanations. These examples reveal model behaviors that numerical feature attribution methods like SHAP might not successfully expose alone. Furthermore, Polyjuice aids in systematic counterfactual error analysis, where patterns across inputs are aggregated to elucidate model discrepancies.

Implications and Future Directions

The implications of this work are profound for several aspects of AI and machine learning:

• Enhanced Model Robustness: Through diverse training data induced by counterfactuals, models become more adaptable to varied linguistic phenomena, reducing susceptibility to spurious biases from training data artifacts.
• Interpretability and Trust: Counterfactuals provide tangible examples of how slight changes in input can affect model behavior, which can foster more interpretable models trusted by human stakeholders.
• Broadened Applications: This work opens pathways for enriched error analysis across different model architectures, extending beyond NLP. The methodology can be adapted for other domains requiring counterfactual reasoning.
• Automated Collaborative Systems: The control mechanisms indicate potential improvements in human-AI teams, where humans and AI collaborate through interactive and targeted counterfactual generation to refine models continually.

Future advancements could focus on diminishing biases in control code distributions and enhancing counterfactual generation systems' efficacy across different contexts and domains. Furthermore, polyjuice's interaction mechanisms could be expanded to enable more sophisticated collaborative human-AI model refinement strategies.