Linguistic Preferences of Post-Hoc Explanation Methods and Their Impact on Disagreement

Core Concepts

Different post-hoc explanation methods systematically target different classes of words, leading to disagreement in their token-level importance attributions. Analyzing the methods at the level of syntactic spans reveals higher agreement, as methods tend to target the same linguistic constituents despite selecting different individual tokens.

Abstract

The paper investigates the linguistic preferences of different post-hoc explanation methods and how they impact the disagreement between the methods. The key findings are:

Different methods have systematic preferences for certain word classes, such as nouns, verbs, or punctuation. Methods can be divided into two groups based on their word class preferences.

Methods that agree more with each other and with human rationales exhibit more similar word class preferences.

Analyzing the methods at the level of syntactic spans rather than individual tokens reveals higher agreement, as methods tend to target the same linguistic constituents despite selecting different individual tokens within those spans.

The authors propose an improved dynamic k estimation algorithm that accounts for negative attribution scores and outperforms a random baseline, both at the token and span level.

The paper provides a linguistic perspective on the disagreement between post-hoc explanation methods, showing that the methods' systematic preferences for certain word classes contribute to their disagreement. Shifting the analysis to the span level helps reconcile some of this disagreement, as methods converge on the same syntactic constituents even if they select different individual tokens.

Stats

The average number of tokens per sentence is 24.4 (range 6-73).
The average number of spans per sentence is 15.3 (range 3-45).
The average ratio of spans to tokens is 0.63 (range 0.23-1.0).

Quotes

"Different methods systematically select different classes of words and that methods that agree most with other methods and with humans display similar linguistic preferences."
"Analyzing the methods at the level of syntactic spans rather than individual tokens reveals higher agreement, as methods tend to target the same linguistic constituents despite selecting different individual tokens within those spans."

Key Insights Distilled From

The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement

by Jonathan Kam... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19424.pdf

Deeper Inquiries

How do the linguistic preferences of post-hoc explanation methods vary across different NLP tasks and datasets?

In the context of post-hoc explanation methods in NLP, linguistic preferences can vary significantly across different tasks and datasets. These preferences are influenced by the specific characteristics of the data and the nature of the task being performed. For example, in the study mentioned, different attribution methods showed varying preferences for word classes such as nouns, verbs, adjectives, adpositions, and determiners. These preferences were observed to be task-specific, with some methods consistently targeting certain word classes over others.
In a broader sense, the linguistic preferences of post-hoc explanation methods can vary based on the complexity of the language used in the dataset, the syntactic structures present in the text, and the specific requirements of the NLP task at hand. For instance, in tasks that involve sentiment analysis, methods may exhibit preferences for adjectives and adverbs to capture the sentiment expressed in the text. On the other hand, in tasks like named entity recognition, methods may focus more on proper nouns and noun phrases to identify entities.
Furthermore, the linguistic preferences of post-hoc explanation methods can also be influenced by the domain of the dataset. For example, in medical text analysis, methods may prioritize certain medical terms or concepts that are crucial for understanding the content. Similarly, in legal text analysis, methods may show preferences for legal terminology and specific syntactic structures commonly found in legal documents.
Overall, the linguistic preferences of post-hoc explanation methods are dynamic and can vary based on the specific characteristics of the NLP task, the dataset being analyzed, and the domain of the text.

How can the insights from this study be leveraged to develop more reliable and faithful post-hoc explanation methods?

The insights from this study provide valuable guidance on how to develop more reliable and faithful post-hoc explanation methods in NLP. Here are some ways in which these insights can be leveraged:

Span-Level Analysis: By focusing on the syntactic spans to which tokens belong, developers can improve the reliability of post-hoc explanation methods. Analyzing spans instead of individual tokens can help in capturing the contextual relationships between words and identifying important phrases or structures in the text.

Dynamic k Estimation: Implementing dynamic k estimation algorithms, as suggested in the study, can enhance the accuracy of selecting important tokens or spans. By dynamically adjusting the number of features considered most important based on the properties of the attribution profile, developers can ensure a more precise and context-aware selection process.

Threshold Setting: Setting appropriate thresholds for global importance in dynamic k estimation is crucial for identifying relevant signals in the text. By experimenting with different threshold values and evaluating their impact on agreement scores, developers can optimize the selection of important features and improve the faithfulness of the explanation methods.

Plausibility Testing: Conducting plausibility tests by comparing method–method and human–method agreement can serve as a proxy for assessing the faithfulness of post-hoc explanation methods. Consistent agreement between methods indicates a higher likelihood of faithfulness in reflecting the model's decision-making process.

Task-Specific Adaptation: Adapting the post-hoc explanation methods to the specific requirements of the NLP task and dataset can enhance their reliability and faithfulness. Considering the linguistic characteristics, syntactic structures, and domain-specific terminology can help in developing more accurate and interpretable explanation methods.

By incorporating these insights into the development process, researchers and developers can create more robust and trustworthy post-hoc explanation methods for interpreting NLP models.

What other factors, beyond linguistic preferences, might contribute to the disagreement between post-hoc explanation methods?

While linguistic preferences play a significant role in the disagreement between post-hoc explanation methods, several other factors can also contribute to this disagreement. These factors include:

Model Complexity: The complexity of the underlying NLP model can impact the attribution scores generated by different methods. Models with intricate architectures or multiple layers may produce diverse attribution patterns, leading to disagreement among explanation methods.

Data Variability: Variations in the dataset, such as the presence of ambiguous or noisy text, can influence the attribution results. Different methods may interpret and prioritize features differently based on the data variability, contributing to disagreement in the explanations.

Feature Interaction: The interaction between features in the input data can affect the attribution scores assigned by post-hoc explanation methods. Methods that consider feature interactions at a higher level (e.g., phrases or syntactic structures) may exhibit different preferences and lead to disagreement.

Algorithmic Differences: Variations in the algorithms and techniques used by different explanation methods can result in disagreement. Methods based on different principles, such as gradient-based approaches, perturbation-based approaches, or model-specific techniques, may produce contrasting results.

Noise Sensitivity: Sensitivity to noise in the data or model predictions can impact the attribution scores and contribute to disagreement. Methods that are more susceptible to noise or outliers may exhibit higher variability in their explanations.

Evaluation Metrics: The choice of evaluation metrics and criteria for assessing the quality of explanations can also influence the disagreement between methods. Different metrics may prioritize certain aspects of the explanations, leading to divergent results.

By considering these additional factors alongside linguistic preferences, researchers and developers can gain a comprehensive understanding of the sources of disagreement between post-hoc explanation methods and work towards improving the reliability and consistency of these methods.