As LLMs are increasingly applied in socially impactful settings, concerns about gender bias have prompted growing efforts both to measure and mitigate such bias.These efforts often rely on evaluation tasks that differ from natural language distributions, as they typically involve carefully constructed task prompts that overtly or covertly signal the presence of gender bias-related content.In this paper, we examine how signaling the evaluative purpose of a task impacts measured gender bias in LLMs.Concretely, we test models under prompt conditions that (1) make the testing context salient, and (2) make gender-focused content salient.We then assess prompt sensitivity across four task formats with both token-probability and discrete-choice metrics.We find that prompts that more clearly align with (gender bias) evaluation framing elicit distinct gender output distributions compared to less evaluation-framed prompts.Discrete-choice metrics further tend to amplify bias relative to probabilistic measures.These findings do not only highlight the brittleness of LLM gender bias evaluations but open a new puzzle for the NLP benchmarking and development community: To what extent can well-controlled testing designs trigger LLM "testing mode" performance, and what does this mean for the ecological validity of future benchmarks.

For the safety assessment of autonomous driving systems (ADS), simulation testing has become an important complementary technique to physical road testing. In essence, simulation testing is a scenario-driven approach, whose effectiveness is highly dependent on the quality of given simulation scenarios. Moreover, simulation scenarios should be encoded into well-formatted files, otherwise, ADS simulation platforms cannot take them as inputs. Without large public datasets of simulation scenario files, both industry and academic applications of ADS simulation testing are hindered.