# Who's Asking? Evaluating LLM Robustness to Inquiry Personas in Factual Question Answering Source: [Who's Asking? Evaluating LLM Robustness to Inquiry Personas in Factual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:57:13.861Z Authors: Nil-Jana Akpinar, Chia-Jung Lee, Vanessa Murdock, Pietro Perona Published: 2025-10-14T18:59:15.000Z ## Abstract Large Language Models (LLMs) should answer factual questions truthfully, grounded in objective knowledge, regardless of user context such as self-disclosed personal information, or system personalization. In this paper, we present the first systematic evaluation of LLM robustness to inquiry personas, i.e. user profiles that convey attributes like identity, expertise, or belief. While prior work has primarily focused on adversarial inputs or distractors for robustness testing, we evaluate plausible, human-centered inquiry persona cues that users disclose in real-world interactions. We find that such cues can meaningfully alter QA accuracy and trigger failure modes such as refusals, hallucinated limitations, and role confusion. These effects highlight how model sensitivity to user framing can compromise factual reliability, and position inquiry persona testing as an effective tool for robustness evaluation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()