Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales
arXiv:2606.01148v1 Announce Type: new 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
The proliferation of complex AI models necessitates more robust and reliable explanation methods for understanding their behavior, making research into explanation quality increasingly critical.
Sophisticated readers should care about this research because it directly impacts the trustworthiness, interpretability, and potential adoption of advanced AI systems, particularly in sensitive applications.
This research refines our understanding of AI explanation mechanisms, suggesting that not all explanations are equally effective for simulating model behavior, which will influence future AI design and evaluation paradigms.
- · AI ethicists
- · Developers of interpretable AI
- · Regulators of AI
- · Users of complex AI systems
- · Developers of uninterpretable AI models
- · Companies relying on black-box AI for critical decisions
Improved methods for evaluating and generating AI explanations will emerge, focusing on their utility for model simulation.
This will lead to the development of AI systems with built-in, more effective explanation capabilities, increasing user trust and adoption.
Enhanced interpretability could enable more complex and autonomous AI agents to operate in sensitive domains, provided their reasoning is sufficiently transparent.
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