Bayesian Concept Bottleneck Models with LLM Priors (2410.15555v1)

Abstract: Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. The standard training procedure for CBMs is to predefine a candidate set of human-interpretable concepts, extract their values from the training data, and identify a sparse subset as inputs to a transparent prediction model. However, such approaches are often hampered by the tradeoff between enumerating a sufficiently large set of concepts to include those that are truly relevant versus controlling the cost of obtaining concept extractions. This work investigates a novel approach that sidesteps these challenges: BC-LLM iteratively searches over a potentially infinite set of concepts within a Bayesian framework, in which LLMs serve as both a concept extraction mechanism and prior. BC-LLM is broadly applicable and multi-modal. Despite imperfections in LLMs, we prove that BC-LLM can provide rigorous statistical inference and uncertainty quantification. In experiments, it outperforms comparator methods including black-box models, converges more rapidly towards relevant concepts and away from spuriously correlated ones, and is more robust to out-of-distribution samples.

• The paper proposes a Bayesian framework integrating LLMs into CBMs to iteratively refine concept sets.
• It employs a Metropolis-within-Gibbs sampling strategy to update concepts while quantifying uncertainties robustly.
• Empirical results show that BC-LLM outperforms traditional CBMs and some black-box models in various classification tasks.

Bayesian Concept Bottleneck Models with LLM Priors

The paper "Bayesian Concept Bottleneck Models with LLM Priors" introduces an innovative approach to enhance Concept Bottleneck Models (CBMs) using LLMs within a Bayesian inference framework. This method addresses the inherent challenges of CBMs, which aim for interpretability without compromising accuracy, by leveraging LLMs to iteratively search for relevant concepts through a potentially infinite set.

Introduction and Motivation

CBMs have emerged as a compromise between interpretability and performance, employing a transparent intermediate layer of human-interpretable concepts. Yet, these models often require predefined sets of concepts, leading to limitations when this set lacks relevance or completeness. The paper highlights the inefficacy of existing CBMs in balancing these trade-offs, particularly in contexts like healthcare, where interpretability is crucial. By integrating LLMs, BC-LLM seeks to bypass these constraints, allowing for dynamic exploration and refinement of concepts.

Methodology

The core contribution is BC-LLM, a framework utilizing LLMs as both a concept extraction mechanism and a Bayesian prior. This model advances over traditional CBMs by:

1. Iterative Concept Searching: Employing a Bayesian inference structure, BC-LLM iteratively updates the set of concepts, driven by data-backed statistical principles.
2. LLM Integration: LLMs serve multifunctional roles, from proposing concepts based on prior knowledge to annotating data, facilitating broad applicability across various data types, including text and images.
3. Statistical Rigor: Despite the imperfections of LLMs, BC-LLM ensures robust statistical inference and quantifies uncertainties, a significant enhancement over previous methods.

The framework operates in iterations, where it refines a concept set based on partial data, applying a Metropolis-within-Gibbs sampling strategy. The paper details the computational refinement processes needed to balance efficiency and exploration of candidate concepts.

Experimental Results

Empirically, BC-LLM demonstrates superior predictive performance and concept recovery across domains, including simulated clinical note analysis and image classification of bird species. The robustness of BC-LLM is evident in its ability to outperform existing CBM methodologies and even some black-box models, showcasing better convergence and out-of-distribution performance.

Implications and Future Directions

The paper's findings suggest notable implications for both practical applications and theoretical advancements in machine learning interpretability. Practically, BC-LLM's ability to dynamically explore and refine concepts could lead to more actionable insights in domains like healthcare. Theoretically, the model paves the way for future explorations of combining LLMs with Bayesian frameworks, possibly guiding the development of even more expansive and adaptable AI systems.

Overall, while BC-LLM marks a significant step forward for interpretability and usability in CBMs, future research may explore enhancing LLM accuracy within this framework or extend the methodology to other complex data environments. This trajectory could redefine the frameworks of interpretability in AI, making machine learning models more transparent and trustable for critical applications.