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Uncertainty-aware Language Guidance for Concept Bottleneck Models

arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive expert knowledge and labor, constraining the broad adoption of CBMs. On the other hand, there are a few works that leverage the knowledge of large language models (LLMs) to construct concept bottlenecks. Nevertheless, they face two essential limitations: First, they overlook the uncertainty associated with the concepts annotated by LLMs and lack a valid mechanism to quantify uncertainty about the annotated concepts, increasing the risk of errors due to hallucinations from LLMs. Additionally, they fail to incorporate the uncertainty associated with these annotations into the learning process for concept bottleneck models. To address these limitations, we propose

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Yangyi Li, Mengdi Huai · · 1 min read · 9 views
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arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive expert knowledge and labor, constraining the broad adoption of CBMs. On the other hand, there are a few works that leverage the knowledge of large language models (LLMs) to construct concept bottlenecks. Nevertheless, they face two essential limitations: First, they overlook the uncertainty associated with the concepts annotated by LLMs and lack a valid mechanism to quantify uncertainty about the annotated concepts, increasing the risk of errors due to hallucinations from LLMs. Additionally, they fail to incorporate the uncertainty associated with these annotations into the learning process for concept bottleneck models. To address these limitations, we propose a novel uncertainty-aware CBM method, which not only rigorously quantifies the uncertainty of LLM-annotated concept labels with valid and distribution-free guarantees, but also incorporates quantified concept uncertainty into the CBM training procedure to account for varying levels of reliability across LLM-annotated concepts. We also provide the theoretical analysis for our proposed method. Extensive experiments on the real-world datasets validate the desired properties of our proposed methods.

Executive Summary

The article proposes a novel uncertainty-aware Concept Bottleneck Model (CBM) method that addresses two essential limitations of existing CBM-based approaches. The proposed method rigorously quantifies the uncertainty of large language model (LLM)-annotated concept labels, ensuring valid and distribution-free guarantees, and incorporates quantified concept uncertainty into the CBM training procedure. This approach aims to mitigate the risk of errors due to hallucinations from LLMs and enhance the reliability of CBMs. The authors provide theoretical analysis for their proposed method and demonstrate its effectiveness through extensive experiments on real-world datasets. This work has significant implications for the development and deployment of CBMs in practical applications.

Key Points

  • ▸ The proposed uncertainty-aware CBM method addresses limitations of existing CBM-based approaches
  • ▸ Quantifies uncertainty of LLM-annotated concept labels with valid and distribution-free guarantees
  • ▸ Incorporates quantified concept uncertainty into the CBM training procedure

Merits

Strength

The proposed method provides a rigorous and distribution-free approach to quantifying uncertainty, ensuring the reliability of CBMs.

Strength

The incorporation of concept uncertainty into the CBM training procedure enhances the adaptability of the model to varying levels of reliability across LLM-annotated concepts.

Demerits

Limitation

The reliance on large language models (LLMs) for concept annotation may introduce additional sources of uncertainty and errors.

Limitation

The proposed method may require significant computational resources and expertise in machine learning and statistical analysis.

Expert Commentary

The article presents a well-motivated and technically sound approach to addressing the limitations of existing CBM-based methods. The proposed uncertainty-aware CBM method demonstrates a clear understanding of the challenges associated with relying on large language models for concept annotation and provides a novel solution to mitigate these risks. The theoretical analysis and experimental results provide strong evidence for the effectiveness of the proposed method. However, as with any machine learning approach, the reliance on large language models and the potential computational requirements should be carefully considered. Additionally, the implications of this work extend beyond the technical aspects, highlighting the importance of uncertainty quantification and explainability in AI development and deployment.

Recommendations

  • ✓ Further research should focus on exploring the application of the proposed method in various domains and evaluating its performance in real-world scenarios.
  • ✓ Developers and practitioners should consider the computational requirements and expertise necessary for implementing the proposed method and ensure that adequate resources are allocated for its effective deployment.

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