Bayesian Concept Bottlenecks with LLM Priors

What this episode covers

This document introduces **BC-LLM**, a novel method for creating **Concept Bottleneck Models (CBMs)** that are both accurate and interpretable. Traditional CBMs rely on a predefined set of concepts, limiting their effectiveness and requiring significant human effort. **BC-LLM** addresses this by integrating **Large Language Models (LLMs)** within a **Bayesian framework** to iteratively discover relevant concepts. The LLMs serve as a **concept extraction mechanism** and provide **prior information**, enabling the model to explore a vast, potentially infinite, concept space. This approach is proven to provide **rigorous statistical inference** and **uncertainty quantification**, even when LLMs are imperfect, and experiments demonstrate its superior performance and robustness compared to existing methods across various data types.

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