Summary of Evaluating Readability and Faithfulness Of Concept-based Explanations, by Meng Li et al.
Evaluating Readability and Faithfulness of Concept-based Explanations
by Meng Li, Haoran Jin, Ruixuan Huang, Zhihao Xu, Defu Lian, Zijia Lin, Di Zhang, Xiting Wang
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 A novel approach is proposed to formalize and evaluate concept-based explanations in Large Language Models (LLMs), which are crucial for understanding high-level patterns learned by these models. The existing methods for evaluating concept-based explanations lack a unified formalization, making it challenging to quantify the faithfulness or readability of these explanations. To address this gap, the authors introduce a formal definition of concepts that generalizes to diverse concept-based explanations’ settings and propose two measures: perturbation-based faithfulness and automatic readability. The proposed measures are evaluated using an optimization problem and meta-evaluation methods, providing insights into the effectiveness of different evaluation approaches. This work has implications for the development and application of LLMs in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can learn complex patterns from text data, but it’s hard to understand what they’ve learned. To fix this, researchers are working on “concept-based explanations” that help us see how these models work. The problem is that we don’t have a good way to test if these explanations are accurate or easy to understand. A new approach tries to solve this by defining what a concept is and then creating two measures: one for how faithful the explanation is, and another for how readable it is. This helps us evaluate whether the explanations are helpful or not. |
Keywords
» Artificial intelligence » Optimization
