Computer Science > Artificial Intelligence
[Submitted on 29 Apr 2024 (v1), last revised 4 Oct 2024 (this version, v3)]
Title:Evaluating Readability and Faithfulness of Concept-based Explanations
View PDF HTML (experimental)Abstract:With the growing popularity of general-purpose Large Language Models (LLMs), comes a need for more global explanations of model behaviors. Concept-based explanations arise as a promising avenue for explaining high-level patterns learned by LLMs. Yet their evaluation poses unique challenges, especially due to their non-local nature and high dimensional representation in a model's hidden space. Current methods approach concepts from different perspectives, lacking a unified formalization. This makes evaluating the core measures of concepts, namely faithfulness or readability, challenging. To bridge the gap, we introduce a formal definition of concepts generalizing to diverse concept-based explanations' settings. Based on this, we quantify the faithfulness of a concept explanation via perturbation. We ensure adequate perturbation in the high-dimensional space for different concepts via an optimization problem. Readability is approximated via an automatic and deterministic measure, quantifying the coherence of patterns that maximally activate a concept while aligning with human understanding. Finally, based on measurement theory, we apply a meta-evaluation method for evaluating these measures, generalizable to other types of explanations or tasks as well. Extensive experimental analysis has been conducted to inform the selection of explanation evaluation measures.
Submission history
From: Meng Li [view email][v1] Mon, 29 Apr 2024 09:20:25 UTC (7,331 KB)
[v2] Tue, 30 Apr 2024 03:31:51 UTC (9,667 KB)
[v3] Fri, 4 Oct 2024 01:21:28 UTC (9,851 KB)
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