Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales

Pingjun Hong; Benjamin Roth

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arXiv · 2606.01148

Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales

Pingjun Hong·Benjamin Roth

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Abstract

Natural-language explanations are often treated as a unified interface for understanding model behavior, but different explanation sources may support simulation in different ways. This paper compares two families of explanations for question answering models: verbalized feature attributions and self-generated rationales. We evaluate them under a shared counterfactual simulation setting, using an LLM judge as predictor and measuring whether it can better predict a model's answers to follow-up questions when given its explanation. Across multiple instruction-tuned models, we analyze how explanation source, verbalization strategy, and feature granularity affect the simulatability of explanations. Our results show that explanation format and granularity affect simulatability: attribution-based explanations and self-generated rationales differ in how much they improve counterfactual prediction, with effects that vary across models and formats.

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Pingjun Hong, Benjamin Roth. 2026-05-31. Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales. https://arxiv.org/abs/2606.01148

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