Most existing benchmarks evaluate social bias in large language models (LLMs) using multiple-choice questions (MCQ) grounded in stereotypical contexts. While MCQ formats enable straightforward quantification, how LLMs manifest social bias in task-specific contexts and under different output constraints remains underexplored. We introduce SCENE, a benchmark for measuring the social bias shown by LLMs in scenario-completion tasks. Given short scenarios involving social groups, models must complete key elements of the situation through two tasks: (i) Character Specification, which asks models to assign social attributes to characters, and (ii) Plot Continuation, which asks models to select the most plausible narrative continuation. Combining templates adapted from prior benchmarks with newly collected templates through a university-wide competition, we construct a dataset of over 75,000 Korean instances. Experiments show that bias measurements are highly sensitive to task context and output constraints, even for identical underlying scenarios, suggesting that widely used MCQ-based evaluations may not fully capture certain manifestations of stereotypes. SCENE is publicly available at https://github.com/DSAIL-SKKU/SCENE-EMNLP-2026.

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings from a university-level competition that aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.