LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals

• URL: http://arxiv.org/abs/2601.10700v2
• Date: Sun, 18 Jan 2026 06:28:47 GMT
• Title: LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals
• Authors: Gilat Toker, Nitay Calderon, Ohad Amosy, Roi Reichart,
• Abstract summary: Concept-based explanations quantify how high-level concepts influence model behavior.<n>Existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy.<n>We introduce a framework for constructing datasets containing structural counterfactual pairs: LIBERTy.
• Score: 18.015918696398085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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