Slogans are concise and memorable catchphrases that play crucial roles in advertising by conveying brand identity and shaping public perception. However, advertising fatigue reduces the effectiveness of repeated slogans, creating a growing demand for novel, creative, and insightful slogan generation. While recent work leverages large language models (LLMs) for this task, existing approaches often generate stylistically repetitive outputs that fall short in distinctiveness and insight.

We argue that effective slogans should balance novelty with familiarity and propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation. Well-known quotes naturally align with slogan-length text, employ rich rhetorical devices, and offer depth and insight, making them a powerful resource for creative generation. Technically, we introduce a framework that decomposes slogan generation into interpretable subtasks with a single-pass prompt, including quote matching, structural decomposition, vocabulary replacement, and remix generation. Extensive automatic and human evaluations demonstrate marginal improvements in diversity, emotional impact, and first-glance human preference over three state-of-the-art LLM baselines.

To facilitate healthcare delivery, language models (LMs) have significant potential for clinical prediction tasks using electronic health records (EHRs). However, in these high-stakes applications, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns, thus increasing the need for good uncertainty modelling of automated clinical predictions. To address this, we consider uncertainty quantification of LMs for EHR tasks in both white-box and black-box settings. We first quantify uncertainty in white-box models, where we have access to model parameters and output logits. We show that an effective reduction of model uncertainty can be achieved by using the proposed multi-tasking and ensemble methods in EHRs. Continuing with this idea, we extend our approach to black-box settings, including popular proprietary LMs such as GPT-4. We validate our framework using longitudinal clinical data from over 6,000 patients across ten clinical prediction tasks. Results show that ensembling methods and multi-task prediction prompts reduce uncertainty across different scenarios. These findings increase model transparency in white-box and black-box settings, thereby advancing reliable AI healthcare.