insight - Explainable artificial intelligence - # Evaluating faithfulness and plausibility of explainability methods in NLP

Reconciling Faithfulness and Plausibility in Explainable AI: An Empirical Study Across NLP Tasks

Q: How can explainability algorithms be optimized to simultaneously achieve high faithfulness and plausibility in a wider range of tasks and models?

Explainability algorithms can be optimized to achieve high faithfulness and plausibility by incorporating a dual-objective approach. One way to do this is by combining different types of explanation methods, such as attention-based, gradient-based, and perturbation-based attributions, to leverage their respective strengths. By integrating these methods, the algorithm can provide a more comprehensive and accurate explanation of the model's decision-making process. Furthermore, it is essential to consider the context and complexity of the task or model being analyzed. Tailoring the explanation methods to the specific characteristics of the task can enhance both faithfulness and plausibility. For instance, in NLP tasks like sentiment analysis or intent detection, focusing on key words or phrases that contribute to the model's prediction can improve the quality of explanations. Additionally, incorporating human feedback and domain knowledge into the optimization process can help bridge the gap between faithfulness and plausibility. By involving domain experts in evaluating and refining the explanations generated by the algorithm, it is possible to ensure that the explanations are not only accurate but also understandable and logical from a human perspective. Overall, a holistic approach that combines different explanation methods, considers task-specific nuances, and incorporates human feedback can help optimize explainability algorithms for high faithfulness and plausibility across a wider range of tasks and models.

Q: How can the potential limitations or drawbacks of using large language models like GPT-4 as the benchmark for plausibility evaluation be addressed, and what are they?

Using large language models like GPT-4 as benchmarks for plausibility evaluation can have several limitations and drawbacks that need to be addressed. One major limitation is the potential bias or lack of diversity in the training data of these models, which can lead to skewed or incomplete evaluations of plausibility. To address this, it is crucial to augment the training data with diverse and representative samples to ensure a more comprehensive evaluation. Another drawback is the interpretability of large language models themselves. While they can generate high-quality annotations and explanations, the inner workings of these models are often complex and opaque, making it challenging to understand how they arrive at certain conclusions. To mitigate this, researchers can focus on developing post-hoc interpretability techniques that provide insights into the decision-making process of these models. Furthermore, the scalability and computational resources required to use large language models like GPT-4 as benchmarks can be a limiting factor. Addressing this issue involves optimizing the algorithms and workflows to be more efficient and cost-effective, as well as exploring alternative approaches that do not rely solely on these resource-intensive models. To address these limitations and drawbacks, researchers can also consider using a combination of different benchmarking methods, including human annotators, smaller language models, and expert evaluations, to provide a more robust and diverse assessment of plausibility in explainability evaluations.

Q: What other factors, beyond faithfulness and plausibility, should be considered when evaluating the quality and usefulness of explainability methods in real-world applications?

In addition to faithfulness and plausibility, several other factors should be considered when evaluating the quality and usefulness of explainability methods in real-world applications: Interpretability: The degree to which the explanations provided by the algorithm are understandable and actionable for end-users, including domain experts and non-experts. Clear and intuitive explanations enhance the usability of the model in practical settings. Robustness: The ability of the explainability method to maintain its performance across different datasets, models, and tasks. Robust explanations ensure consistency and reliability in various scenarios. Scalability: The efficiency and scalability of the explainability algorithm in handling large datasets and complex models. Scalable methods can be applied to real-world applications with varying degrees of complexity. Transparency: The transparency of the algorithm in terms of its inner workings and assumptions. Transparent explanations help build trust and confidence in the model's decision-making process. Ethical Considerations: The ethical implications of the explanations provided by the algorithm, including issues related to bias, fairness, and privacy. Ensuring that the explanations are unbiased and fair is crucial for ethical AI deployment. By considering these additional factors alongside faithfulness and plausibility, researchers and practitioners can evaluate the overall quality and usefulness of explainability methods in real-world applications more comprehensively.

Core Concepts

Faithfulness and plausibility can be complementary objectives in explainable AI, as traditional perturbation-based methods like Shapley value and LIME can achieve high levels of both accuracy and user accessibility in their explanations.

Abstract

The study investigates the relationship between faithfulness and plausibility in explainable AI across three NLP tasks: sentiment analysis, intent detection, and topic labeling. The authors utilize GPT-4 to construct professional explanations as benchmarks for plausibility evaluation.

Key highlights:

Faithfulness evaluation shows that Shapley value, LIME, and GPT-4 outperform traditional gradient-based and attention-based methods across the datasets.
Plausibility evaluation based on rank correlation and overlap rate indicates significant overlap between Shapley value, LIME, and GPT-4 in identifying the most influential features.
The findings suggest that faithfulness and plausibility can be complementary objectives, and explainability algorithms can be optimized to achieve high performance in both dimensions.

The authors also discuss limitations, noting that the study focuses on a selected set of tasks and models, and future research could explore more diverse settings. Additionally, how to optimize explainability algorithms for multiple objectives simultaneously requires further investigation.

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Stats

The model's output probability on the predicted class decreases by 5.9748 on average after replacing the top k influential words in the text sequence for BERT on SST-2.
The model's output probability on the predicted class decreases by 5.4660 on average after replacing the top k influential words in the text sequence for RoBERTa on SST-2.
The model's output probability on the predicted class decreases by 3.2694 on average after replacing the top k influential words in the text sequence for BERT on 20Newsgroups.
The model's output probability on the predicted class decreases by 3.4327 on average after replacing the top k influential words in the text sequence for RoBERTa on 20Newsgroups.

Quotes

"Our findings suggest that plausibility and faithfulness can be complementary. The explainability method could achieve a high overlap rate in identifying influential features and also tend to provide explanations that are plausible to human interpreters, which implies that the explainability algorithms can be optimized toward the dual objective of faithfulness and plausibility."

Key Insights Distilled From

Does Faithfulness Conflict with Plausibility? An Empirical Study in Explainable AI across NLP Tasks

by Xiaolei Lu,J... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00140.pdf

Does Faithfulness Conflict with Plausibility? An Empirical Study in Explainable AI across NLP Tasks

Deeper Inquiries

How can explainability algorithms be optimized to simultaneously achieve high faithfulness and plausibility in a wider range of tasks and models?

Explainability algorithms can be optimized to achieve high faithfulness and plausibility by incorporating a dual-objective approach. One way to do this is by combining different types of explanation methods, such as attention-based, gradient-based, and perturbation-based attributions, to leverage their respective strengths. By integrating these methods, the algorithm can provide a more comprehensive and accurate explanation of the model's decision-making process.
Furthermore, it is essential to consider the context and complexity of the task or model being analyzed. Tailoring the explanation methods to the specific characteristics of the task can enhance both faithfulness and plausibility. For instance, in NLP tasks like sentiment analysis or intent detection, focusing on key words or phrases that contribute to the model's prediction can improve the quality of explanations.
Additionally, incorporating human feedback and domain knowledge into the optimization process can help bridge the gap between faithfulness and plausibility. By involving domain experts in evaluating and refining the explanations generated by the algorithm, it is possible to ensure that the explanations are not only accurate but also understandable and logical from a human perspective.
Overall, a holistic approach that combines different explanation methods, considers task-specific nuances, and incorporates human feedback can help optimize explainability algorithms for high faithfulness and plausibility across a wider range of tasks and models.

How can the potential limitations or drawbacks of using large language models like GPT-4 as the benchmark for plausibility evaluation be addressed, and what are they?

Using large language models like GPT-4 as benchmarks for plausibility evaluation can have several limitations and drawbacks that need to be addressed. One major limitation is the potential bias or lack of diversity in the training data of these models, which can lead to skewed or incomplete evaluations of plausibility. To address this, it is crucial to augment the training data with diverse and representative samples to ensure a more comprehensive evaluation.
Another drawback is the interpretability of large language models themselves. While they can generate high-quality annotations and explanations, the inner workings of these models are often complex and opaque, making it challenging to understand how they arrive at certain conclusions. To mitigate this, researchers can focus on developing post-hoc interpretability techniques that provide insights into the decision-making process of these models.
Furthermore, the scalability and computational resources required to use large language models like GPT-4 as benchmarks can be a limiting factor. Addressing this issue involves optimizing the algorithms and workflows to be more efficient and cost-effective, as well as exploring alternative approaches that do not rely solely on these resource-intensive models.
To address these limitations and drawbacks, researchers can also consider using a combination of different benchmarking methods, including human annotators, smaller language models, and expert evaluations, to provide a more robust and diverse assessment of plausibility in explainability evaluations.

What other factors, beyond faithfulness and plausibility, should be considered when evaluating the quality and usefulness of explainability methods in real-world applications?

In addition to faithfulness and plausibility, several other factors should be considered when evaluating the quality and usefulness of explainability methods in real-world applications:

Interpretability: The degree to which the explanations provided by the algorithm are understandable and actionable for end-users, including domain experts and non-experts. Clear and intuitive explanations enhance the usability of the model in practical settings.

Robustness: The ability of the explainability method to maintain its performance across different datasets, models, and tasks. Robust explanations ensure consistency and reliability in various scenarios.

Scalability: The efficiency and scalability of the explainability algorithm in handling large datasets and complex models. Scalable methods can be applied to real-world applications with varying degrees of complexity.

Transparency: The transparency of the algorithm in terms of its inner workings and assumptions. Transparent explanations help build trust and confidence in the model's decision-making process.

Ethical Considerations: The ethical implications of the explanations provided by the algorithm, including issues related to bias, fairness, and privacy. Ensuring that the explanations are unbiased and fair is crucial for ethical AI deployment.

By considering these additional factors alongside faithfulness and plausibility, researchers and practitioners can evaluate the overall quality and usefulness of explainability methods in real-world applications more comprehensively.