innsikt - Natural Language Processing - # Faithfulness of Explanations in Language Models

Measuring the Faithfulness of Free-Text Explanations in Large Language Models

Q: How could the proposed faithfulness metrics be extended to handle more complex forms of explanations, such as structured or multi-sentence outputs?

The proposed faithfulness metrics, such as Correlational Explanatory Faithfulness (CEF) and Correlational Counterfactual Test (CCT), can be extended to handle more complex forms of explanations by adapting the intervention impact measure and explanation mention measure to accommodate structured or multi-sentence outputs. For structured explanations, the intervention impact measure could consider the impact of changes in the overall structure or components of the explanation, rather than just individual words or phrases. This could involve analyzing the coherence and relevance of different parts of the structured explanation to the model's predictions. In the case of multi-sentence outputs, the explanation mention measure could be modified to evaluate how well the different sentences in the explanation collectively capture the factors influencing the model's predictions. This could involve assessing the consistency and completeness of information across multiple sentences. Additionally, for more complex forms of explanations, the metrics could incorporate semantic similarity measures to compare the content of the explanation with the model's reasoning process. This would help in evaluating the faithfulness of explanations that involve nuanced or interconnected information.

Q: What are the potential limitations or biases introduced by the specific counterfactual interventions used in this study, and how could they be addressed?

The specific counterfactual interventions used in the study, such as inserting random adjectives or adverbs, may introduce limitations and biases in assessing faithfulness. One limitation is that these interventions may not fully capture the range of factors influencing the model's predictions, as they are limited to single-word modifications. This could lead to a narrow evaluation of the model's reasoning process. Another potential bias is that the random nature of the interventions may not reflect real-world scenarios or the context of the tasks being evaluated. The interventions may introduce irrelevant or unrealistic changes that do not align with the natural distribution of inputs in the dataset, affecting the validity of the faithfulness assessment. To address these limitations and biases, alternative intervention strategies could be explored, such as using domain-specific perturbations or contextually relevant modifications. These interventions could be designed to mimic more realistic scenarios or potential sources of bias in the model's decision-making process. Additionally, incorporating human-in-the-loop validation of interventions and explanations could help mitigate biases by ensuring that the interventions are meaningful and relevant to the task at hand. Human annotators could provide feedback on the appropriateness of the interventions and the faithfulness of the resulting explanations.

Q: Given the observed variance in faithfulness across datasets, what other factors (e.g. task difficulty, annotation quality) might influence the faithfulness of language model explanations, and how could these be further investigated?

The observed variance in faithfulness across datasets could be influenced by various factors, including task difficulty, annotation quality, dataset characteristics, and model capabilities. To further investigate these influences on the faithfulness of language model explanations, the following factors could be considered: Task Complexity: More complex tasks may require deeper reasoning and understanding, leading to challenges in generating faithful explanations. Investigating the relationship between task complexity and faithfulness could provide insights into the limitations of current models. Annotation Quality: The quality of the annotated explanations in the datasets could impact the faithfulness assessment. Conducting a thorough analysis of the annotation process, inter-annotator agreement, and annotation guidelines could help identify potential sources of bias or inconsistency. Dataset Characteristics: The nature of the dataset, such as the diversity of examples, the presence of ambiguous instances, and the distribution of classes, could affect the faithfulness of explanations. Analyzing dataset biases and characteristics could reveal patterns in the model's behavior. Model Capabilities: The size and architecture of the language model, as well as the training data it was exposed to, can influence the quality of explanations. Comparing different models and their performance on various datasets could shed light on the impact of model capabilities on faithfulness. By systematically investigating these factors and conducting controlled experiments, researchers can gain a deeper understanding of the nuances affecting the faithfulness of language model explanations. This holistic approach could lead to more robust evaluation metrics and insights into improving the interpretability of AI systems.

Grunnleggende konsepter

Explanations provided by large language models may not faithfully capture the factors responsible for their predictions. This work introduces a novel metric, Correlational Explanatory Faithfulness (CEF), to better assess the faithfulness of free-text explanations by accounting for both the impact of input features on model predictions and the frequency with which explanations mention those features.

Sammendrag

The authors argue that in order for explanations provided by large language models to be informatively faithful, it is not enough to simply test whether they mention significant factors - we also need to test whether they mention significant factors more often than insignificant ones.

The paper makes the following key contributions:

It introduces Correlational Explanatory Faithfulness (CEF), a novel faithfulness metric that improves upon prior work by capturing both the degree of impact of input features on model predictions, as well as the difference in explanation mention frequency between impactful and non-impactful factors.
It introduces the Correlational Counterfactual Test (CCT), where CEF is instantiated on the Counterfactual Test (CT) from prior work, using statistical distance between predictions to measure impact.
It runs experiments with the Llama2 family of language models on three datasets and demonstrates that CCT captures faithfulness trends that the existing faithfulness metric used in CT misses.

The authors find that model explanations are more likely to mention inserted words when they're more impactful to the model's predictions, suggesting a degree of faithfulness that increases with model size. However, there is significant variance between datasets, which could be due to the nature of the task or the annotator-provided explanations.

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arxiv.org

Statistikk

"seashells are found by the ocean"
"There are many ways to get around, such as buses, trains, bicycles, etc."
"Piranhas are dangerous"

Sitater

"In order to oversee advanced AI systems, it is important to understand their underlying decision-making process."
"If we can ensure that explanations are faithful to the inner-workings of the models, we could use the explanations as a channel for oversight, scanning them for elements we do not approve of, e.g. racial or gender bias, deception, or power-seeking."
"Being a correlation, it lies in the interval [-1, 1], with 0 indicating no relationship and positive values indicating higher mention importance for more impactful interventions."

Viktige innsikter hentet fra

The Probabilities Also Matter

by Noah Y. Sieg... klokken arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03189.pdf

Dypere Spørsmål

How could the proposed faithfulness metrics be extended to handle more complex forms of explanations, such as structured or multi-sentence outputs?

The proposed faithfulness metrics, such as Correlational Explanatory Faithfulness (CEF) and Correlational Counterfactual Test (CCT), can be extended to handle more complex forms of explanations by adapting the intervention impact measure and explanation mention measure to accommodate structured or multi-sentence outputs.
For structured explanations, the intervention impact measure could consider the impact of changes in the overall structure or components of the explanation, rather than just individual words or phrases. This could involve analyzing the coherence and relevance of different parts of the structured explanation to the model's predictions.
In the case of multi-sentence outputs, the explanation mention measure could be modified to evaluate how well the different sentences in the explanation collectively capture the factors influencing the model's predictions. This could involve assessing the consistency and completeness of information across multiple sentences.
Additionally, for more complex forms of explanations, the metrics could incorporate semantic similarity measures to compare the content of the explanation with the model's reasoning process. This would help in evaluating the faithfulness of explanations that involve nuanced or interconnected information.

What are the potential limitations or biases introduced by the specific counterfactual interventions used in this study, and how could they be addressed?

The specific counterfactual interventions used in the study, such as inserting random adjectives or adverbs, may introduce limitations and biases in assessing faithfulness. One limitation is that these interventions may not fully capture the range of factors influencing the model's predictions, as they are limited to single-word modifications. This could lead to a narrow evaluation of the model's reasoning process.
Another potential bias is that the random nature of the interventions may not reflect real-world scenarios or the context of the tasks being evaluated. The interventions may introduce irrelevant or unrealistic changes that do not align with the natural distribution of inputs in the dataset, affecting the validity of the faithfulness assessment.
To address these limitations and biases, alternative intervention strategies could be explored, such as using domain-specific perturbations or contextually relevant modifications. These interventions could be designed to mimic more realistic scenarios or potential sources of bias in the model's decision-making process.
Additionally, incorporating human-in-the-loop validation of interventions and explanations could help mitigate biases by ensuring that the interventions are meaningful and relevant to the task at hand. Human annotators could provide feedback on the appropriateness of the interventions and the faithfulness of the resulting explanations.

Given the observed variance in faithfulness across datasets, what other factors (e.g. task difficulty, annotation quality) might influence the faithfulness of language model explanations, and how could these be further investigated?

The observed variance in faithfulness across datasets could be influenced by various factors, including task difficulty, annotation quality, dataset characteristics, and model capabilities. To further investigate these influences on the faithfulness of language model explanations, the following factors could be considered:

Task Complexity: More complex tasks may require deeper reasoning and understanding, leading to challenges in generating faithful explanations. Investigating the relationship between task complexity and faithfulness could provide insights into the limitations of current models.

Annotation Quality: The quality of the annotated explanations in the datasets could impact the faithfulness assessment. Conducting a thorough analysis of the annotation process, inter-annotator agreement, and annotation guidelines could help identify potential sources of bias or inconsistency.

Dataset Characteristics: The nature of the dataset, such as the diversity of examples, the presence of ambiguous instances, and the distribution of classes, could affect the faithfulness of explanations. Analyzing dataset biases and characteristics could reveal patterns in the model's behavior.

Model Capabilities: The size and architecture of the language model, as well as the training data it was exposed to, can influence the quality of explanations. Comparing different models and their performance on various datasets could shed light on the impact of model capabilities on faithfulness.

By systematically investigating these factors and conducting controlled experiments, researchers can gain a deeper understanding of the nuances affecting the faithfulness of language model explanations. This holistic approach could lead to more robust evaluation metrics and insights into improving the interpretability of AI systems.