Summary of Bayesian Concept Bottleneck Models with Llm Priors, by Jean Feng et al.
Bayesian Concept Bottleneck Models with LLM Priors
by Jean Feng, Avni Kothari, Luke Zier, Chandan Singh, Yan Shuo Tan
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a new approach to Concept Bottleneck Models (CBMs) that aims to achieve interpretability without sacrificing accuracy. The standard training procedure for CBMs involves predefining a candidate set of human-interpretable concepts and extracting their values from the training data. However, this method is often limited by the tradeoff between including relevant concepts and controlling the cost of obtaining concept extractions. To address this challenge, the authors introduce BC-LLM, an iterative framework that searches over a potentially infinite set of concepts within a Bayesian framework using Large Language Models (LLMs) as both a concept extraction mechanism and prior. The authors demonstrate that BC-LLM can provide rigorous statistical inference and uncertainty quantification, outperforms comparator methods, and is more robust to out-of-distribution samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make machines understand what they’re learning from data. It’s called Concept Bottleneck Models (CBMs), which tries to balance being understandable with being accurate. The usual method for CBMs involves picking some important ideas beforehand and finding values for them in the data. However, this has limitations because it needs to find a good balance between including important concepts and not using too much time or resources. To solve this problem, the authors created BC-LLM, which is an iterative process that can explore many possible concepts. This approach uses Large Language Models (LLMs) as both a way to find these ideas and a guide for what’s important. The results show that this new method works better than others, is more accurate, and can handle unexpected situations. |
Keywords
» Artificial intelligence » Inference
