Adversarial Attacks Undermine Explanation Robustness of Rationalization Models

Concetti Chiave

Existing rationalization models are vulnerable to adversarial attacks that can significantly change the selected rationales while maintaining model predictions, undermining the credibility of these models.

Sintesi

The paper investigates the explanation robustness of rationalization models, which select a subset of input text as rationale to provide explanations for model predictions. The authors propose UAT2E, a variant of Universal Adversarial Triggers, to conduct both non-target and target attacks on the explanations of rationalization models.

The key findings are:

1. Existing rationalization models exhibit significant fragility in explanation robustness, even when predictions remain unchanged. The attacks can lead the models to select more meaningless tokens or triggers as rationales.

2. The explanation vulnerability of rationalization models arises from their inherent defects such as degeneration and spurious correlation, which are exacerbated by the attacks.

3. Using powerful encoders like BERT or supervised training with human-annotated rationales does not guarantee robustness of explanations; instead, it makes the explanations more susceptible to attack influence.

4. Enhancing prediction robustness through adversarial training does not effectively improve explanation robustness.

5. The gradient-based search used in UAT2E is effective in identifying optimal triggers to undermine explanation robustness.

Based on these findings, the authors provide recommendations to improve the explanation robustness of rationalization models, including conducting rigorous evaluations, exploring defense mechanisms, and establishing robust evaluation benchmarks and metrics.

Statistiche

The paper does not contain any specific numerical data or statistics to support the key findings. The analysis is based on qualitative observations and comparisons of various evaluation metrics across different models, datasets, and attack settings.

Citazioni

"Existing rationalization models are vulnerable to the attacks on explanation including both non-target and target attacks."
"Using powerful encoders such as BERT and supervised training with human-annotated rationales in rationalization models does not guarantee the robustness of explanations; instead, it makes the explanation more susceptible to influence of attack."
"Enhancing prediction robustness through adversarial training does not significantly improve explanation robustness."

Approfondimenti chiave tratti da

Adversarial Attack for Explanation Robustness of Rationalization Models

by Yuankai Zhan... alle arxiv.org 09-19-2024

https://arxiv.org/pdf/2408.10795.pdf

Domande più approfondite

How can we develop effective defense mechanisms to protect rationalization models from adversarial attacks on explanations?

To develop effective defense mechanisms against adversarial attacks on explanations in rationalization models, several strategies can be employed:

Adversarial Training: Incorporating adversarial examples during the training phase can enhance the robustness of rationalization models. By exposing the model to various adversarial inputs, it can learn to maintain the integrity of its explanations even when faced with crafted attacks. This approach can be complemented by using a diverse set of adversarial examples to cover a wide range of potential attack scenarios.

Regularization Techniques: Implementing regularization methods that promote the selection of meaningful tokens can help mitigate the effects of adversarial attacks. Techniques such as sparsity regularization can encourage the model to focus on a smaller, more relevant subset of input tokens, thereby reducing the likelihood of selecting spurious or meaningless tokens during an attack.

Robustness Evaluation Benchmarks: Establishing standardized benchmarks for evaluating the robustness of rationalization models against adversarial attacks is crucial. These benchmarks should include metrics that specifically assess explanation quality under attack conditions, allowing researchers to identify weaknesses and improve model designs accordingly.

Explainability-Aware Training: Training models with a focus on both prediction accuracy and explanation quality can lead to more resilient rationalization models. This can involve using human-annotated rationales as part of the training data, ensuring that the model learns to generate explanations that are not only accurate but also interpretable and robust against manipulation.

Dynamic Masking Strategies: Implementing dynamic masking strategies that adaptively adjust the rationale selection process based on the input context can help in maintaining the integrity of explanations. By continuously evaluating the relevance of selected tokens, the model can avoid being misled by adversarial triggers.

Ensemble Methods: Utilizing ensemble techniques that combine multiple rationalization models can enhance robustness. By aggregating the outputs of different models, the ensemble can provide a more stable and reliable explanation, reducing the impact of adversarial attacks on any single model.

What are the potential limitations or drawbacks of the proposed UAT2E attack method, and how can it be further improved?

The UAT2E attack method, while effective in demonstrating the vulnerabilities of rationalization models, has several limitations and potential drawbacks:

Dependence on Model Structure: UAT2E assumes white-box access to the rationalization model, which may not always be feasible in real-world applications. This reliance on model transparency limits the applicability of the attack method to scenarios where the model's internal workings are known.

Specificity of Triggers: The effectiveness of UAT2E is contingent upon the identification of optimal triggers that can manipulate the rationale without altering predictions. However, the specificity of these triggers may limit their generalizability across different models or datasets, reducing the attack's overall effectiveness.

Potential for Overfitting: The iterative nature of the trigger search process may lead to overfitting to specific datasets or model architectures. This could result in a lack of robustness when applied to unseen data or different model configurations.

Limited Scope of Attacks: UAT2E primarily focuses on non-target and target attacks on explanations. Expanding the scope to include more diverse attack strategies, such as multi-modal attacks or attacks that consider contextual information, could enhance its effectiveness.

Evaluation Metrics: The evaluation of the attack's success relies heavily on specific metrics, such as Gold Rationale F1 (GR) and Attack Capture Rate (AR). Developing more comprehensive metrics that capture the nuances of explanation quality and robustness could provide a clearer picture of the attack's impact.

To improve UAT2E, future work could focus on:

Developing Black-Box Attack Variants: Creating versions of UAT2E that do not require white-box access to the model could broaden its applicability and relevance in real-world scenarios.

Incorporating Contextual Information: Enhancing the attack strategy to consider the context of the input text could lead to more effective trigger generation and manipulation of rationales.

Exploring Transferability: Investigating the transferability of identified triggers across different models and datasets could provide insights into the generalizability of the attack method.

What are the broader implications of the fragility of explanations in rationalization models, and how might this impact the adoption and trust in these models in real-world applications?

The fragility of explanations in rationalization models has significant implications for their adoption and trust in real-world applications:

Erosion of Trust: If rationalization models consistently produce explanations that can be easily manipulated or rendered meaningless through adversarial attacks, users may lose trust in these systems. Trust is crucial for the deployment of AI in sensitive domains such as healthcare, finance, and law, where stakeholders rely on transparent and reliable decision-making processes.

Regulatory Compliance: As regulatory frameworks increasingly demand explainability in AI systems, the inability of rationalization models to provide robust explanations could hinder compliance with legal and ethical standards. This may result in organizations being reluctant to adopt such models, fearing potential legal repercussions.

Impact on User Acceptance: Users are more likely to accept and utilize AI systems that provide clear and trustworthy explanations for their predictions. The fragility of explanations can lead to skepticism among users, reducing the likelihood of widespread adoption of rationalization models in practical applications.

Increased Scrutiny and Research Focus: The vulnerabilities exposed by the fragility of explanations may lead to increased scrutiny from researchers, practitioners, and regulatory bodies. This could drive further research into developing more robust rationalization models and defense mechanisms, ultimately benefiting the field of explainable AI.

Potential for Misuse: The ability to manipulate explanations through adversarial attacks raises concerns about the potential misuse of rationalization models. Malicious actors could exploit these vulnerabilities to undermine the credibility of AI systems, leading to misinformation and harmful consequences.

Need for Comprehensive Evaluation: The recognition of explanation fragility emphasizes the need for comprehensive evaluation frameworks that assess both prediction accuracy and explanation robustness. This could lead to the development of more resilient models that are better suited for real-world applications.

In conclusion, addressing the fragility of explanations in rationalization models is essential for fostering trust, ensuring compliance, and promoting the responsible use of AI technologies in various sectors.