Bayesian Concept Bottleneck Models with LLM Priors
Jean Feng, Avni Kothari, Luke Zier, Chandan Singh, Yan Shuo Tan·October 21, 2024
Summary
Bayesian Concept Bottleneck Models with Large Language Model priors (BC-LLM) are advanced techniques that balance interpretability and accuracy, extracting human-readable concepts from data for transparent prediction. BC-LLM iteratively searches through a vast concept set using Bayesian methods and Large Language Models as priors, making it broadly applicable and multi-modal. Despite Large Language Model imperfections, BC-LLM provides rigorous statistical inference and uncertainty quantification, outperforming black-box models in experiments, converging faster to relevant concepts, and being more robust to out-of-distribution samples.
Introduction
Background
Overview of Bayesian Concept Bottleneck Models
Role of Large Language Models in BC-LLM
Objective
Aim of using BC-LLM in data analysis
Importance of interpretability and accuracy in model predictions
Method
Data Collection
Sources of data for BC-LLM
Preprocessing steps for data readiness
Data Preprocessing
Techniques for cleaning and transforming data
Feature extraction methods for multi-modal data
Bayesian Search
Overview of Bayesian search algorithms
Integration of Large Language Models as priors
Concept Extraction
Process of extracting human-readable concepts
Iterative refinement of concept set
Statistical Inference
Techniques for rigorous statistical inference
Uncertainty quantification in predictions
Model Evaluation
Metrics for assessing model performance
Comparison with black-box models
Applications
Multi-modal Data Analysis
Handling text, images, and other data types
Case studies in diverse fields
Robustness Testing
Evaluating model performance on out-of-distribution samples
Strategies for improving robustness
Conclusion
Summary of BC-LLM benefits
Future directions and research opportunities
Practical implications for data science and machine learning
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What are Bayesian Concept Bottleneck Models with Large Language Model priors (BC-LLM)?
How does BC-LLM outperform black-box models in experiments, and what advantages does it offer in terms of convergence and robustness to out-of-distribution samples?
What makes BC-LLM broadly applicable and multi-modal?
How do BC-LLM balance interpretability and accuracy in extracting human-readable concepts from data?