Abstract
Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causal effects estimated from counterfactuals. In practice, existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy. To address this, we introduce a framework for constructing datasets containing structural counterfactual pairs: LIBERTy (LLM-based Interventional Benchmark for Explainability with Reference Targets). LIBERTy is grounded in explicitly defined Structured Causal Models (SCMs) of the text generation, interventions on a concept propagate through the SCM until an LLM generates the counterfactual. We introduce three datasets (disease detection, CV screening, and workplace violence prediction) together with a new evaluation metric, order-faithfulness. Using them, we evaluate a wide range of methods across five models and identify substantial headroom for improving concept-based explanations. LIBERTy also enables systematic analysis of model sensitivity to interventions: we find that proprietary LLMs show markedly reduced sensitivity to demographic concepts, likely due to post-training mitigation. Overall, LIBERTy provides a much-needed benchmark for developing faithful explainability methods.
Abstract (translated)
基于概念的解释量化了高层次概念（如性别或经验）如何影响模型的行为，这对高风险领域的决策者来说至关重要。最近的研究通过将这些解释与从反事实估计出的参考因果效应进行比较来评估其准确性。然而，在实践中，现有的基准测试依赖于成本高昂且不完美的手工编写反事实数据作为替代方案。为了解决这个问题，我们引入了一个框架用于构建包含结构化反事实对的数据集：LIBERTy（基于LLM的可解释性干预基准，带有参考目标）。LIBERTy 以明确定义的文本生成结构因果模型（SCMs）为基础，在概念上进行干预时，这些变化会沿着 SCM 传播直至 LLM 生成反事实。我们介绍了三个数据集（疾病检测、简历筛选和工作场所暴力预测），并提出了一种新的评估指标：顺序忠实度。通过使用这些数据集，我们在五种模型中广泛的方法范围内进行了评价，并发现提高基于概念的解释的空间仍然很大。此外，LIBERTy 还允许对模型对干预的敏感性进行系统分析：我们发现在专有 LLM 中，人口统计学概念上的敏感度显著降低，这可能是由于训练后的缓解措施所导致。总体而言，LIBERTy 为开发准确的可解释方法提供了一个急需的基准测试标准。
URL
https://arxiv.org/abs/2601.10700
