Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation
New research shows that prompt robustness in LLMs differs significantly between objective and subjective questions, with variations across models, datasets, and prompt modifications. The study warns against interpreting model responses to subjective questions as direct indicators of beliefs.
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[Submitted on 6 Jul 2026]
Title:Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation
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Abstract:Survey-style evaluations of large language models often treat a prompted response as a measure of a model's values or beliefs. This assumption is particularly fragile when responses are read as evidence of political values, social attitudes, or beliefs. We ask whether prompt robustness differs between objective questions with fixed answers and subjective questions that ask for opinions or values. We evaluate four instruction-tuned model families on three objective datasets (MMLU, ARC, and CulturalBench) and three subjective datasets (Political Compass Test, ValueBench, and World Values Survey). For each question/statement, we apply multiple types of prompt changes, such as variations in wording, framing, and format, and measure whether the model gives the same answer across variants. Using a binomial generalized estimating equation, we find significant effects of model, dataset, prompt category, and their interactions. The dataset type effect is also significant, and the interaction between dataset type and prompt category is large. These results show that prompt robustness depends on the question type, the prompt change, and the model.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05554 [cs.CL]
(or arXiv:2607.05554v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.05554
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sadia Kamal [view email] [v1] Mon, 6 Jul 2026 18:47:47 UTC (57 KB)
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