aiqbench analysis
This research identifies critical flaws in using LLM simulators to evaluate model explanations, revealing that simulators often bypass explanations by leveraging task priors or leaking hidden label mappings. These findings suggest current automated protocols may overestimate explanation quality without robust safeguards against shortcut learning. The paper offers concrete recommendations for designing more reliable evaluation frameworks essential for trustworthy AI systems.
Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations
arXiv:2609.08585v1 Announce Type: cross Abstract: Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is costly, automated simulatability replaces human explainees with LLM simulators, as proposed in ConSim (Poch\'e et al., 2025) for large-scale experiments. We qualitatively replicate and extend ConSim's ranking of explanation methods across the tested datasets, explanation families, and simulator LLMs, and identify two limitations. First, when class names are meaningful, simulators can obtain high simulatability by solving the classification task directly, without relying on the explanations. Second, class anonymization can reward explanations for leaking the hidden label mapping, a limitation we expose with a new classes-as-concepts baseline. These results are consistent with a shortcut hypothesis: in the tested settings, simulator predictions mainly rely on task priors, while explanations produce small changes. We derive recommendations for more robust automated simulatability evaluations.
Abstract:Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is costly, automated simulatability replaces human explainees with LLM simulators, as proposed in ConSim (Poché et al., 2025) for large-scale experiments. We qualitatively replicate and extend ConSim's ranking of explanation methods across the tested datasets, explanation families, and simulator LLMs, and identify two limitations. First, when class names are meaningful, simulators can obtain high simulatability by solving the classification task directly, without relying on the explanations. Second, class anonymization can reward explanations for leaking the hidden label mapping, a limitation we expose with a new classes-as-concepts baseline. These results are consistent with a shortcut hypothesis: in the tested settings, simulator predictions mainly rely on task priors, while explanations produce small changes. We derive recommendations for more robust automated simulatability evaluations.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2609.08585 [cs.CL]
(or arXiv:2609.08585v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.08585 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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