Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values.However, existing alignment paradigms often model an averaged or monolithic preference, failing to account for the diversity of perspectives across cultures, demographics, and communities.This limitation is particularly critical in health-related scenarios, where plurality is essential due to the influence of culture, religion, personal values, and conflicting opinions.Despite progress in pluralistic alignment, no prior work has focused on health, likely due to the unavailability of publicly available datasets.To address this gap, we introduce VITAL, a new benchmark dataset comprising 13.1K value-laden situations and 5.4K multiplechoice questions focused on health, designed to assess and benchmark pluralistic alignment methodologies.Through extensive evaluation of eight LLMs of varying sizes, we demonstrate that existing pluralistic alignment techniques fall short in effectively accommodating diverse healthcare beliefs, underscoring the need for tailored AI alignment in specific domains.This work highlights the limitations of current approaches and lays the groundwork for developing health-specific alignment solutions.1

Embeddings-as-a-Service (EaaS) is a service offered by large language model (LLM) developers to supply embeddings generated by LLMs.Previous research suggests that EaaS is prone to imitation attacks-attacks that clone the underlying EaaS model by training another model on the queried embeddings.As a result, EaaS watermarks are introduced to protect the intellectual property of EaaS providers.In this paper, we first show that existing EaaS watermarks can be removed by paraphrasing when attackers clone the model.Subsequently, we propose a novel watermarking technique that involves linearly transforming the embeddings, and show that it is empirically and theoretically robust against paraphrasing.1 Training Dataset ` Verification Dataset 0.4 0.6 0 0 0.4 0.6 0.6 0 0.4 0.57 -0.85 1.28 1.28 0.57 -0.85 -0.85 1.28 0.57 Secret Linear TransformationInverse Linear Transformation

As large language models are increasingly deployed in sensitive domains such as healthcare, ensuring their outputs reflect the diverse values and perspectives held across populations is critical. However, existing alignment approaches, including pluralistic paradigms like Modular Pluralism, often fall short in the health domain, where personal, cultural, and situational factors shape pluralism. Motivated by the aforementioned healthcare challenges, we propose a first lightweight, generalizable, pluralistic alignment approach, ETHOSAGENTS, designed to simulate diverse perspectives and values. We empirically show that it advances the pluralistic alignment for all three modes across seven varying-sized open and closed models. Our findings reveal that health-related pluralism demands adaptable and normatively aware approaches, offering insights into how these models can better respect diversity in other high-stakes domains.

Embedding as a Service (EaaS) has become a widely adopted solution, which offers feature extraction capabilities for addressing various downstream tasks in Natural Language Processing (NLP).Prior studies have shown that EaaS can be prone to model extraction attacks; nevertheless, this concern could be mitigated by adding backdoor watermarks to the text embeddings and subsequently verifying the attack models post-publication.Through the analysis of the recent watermarking strategy for EaaS, EmbMarker, we design a novel CSE (Clustering, Selection, Elimination) attack that removes the backdoor watermark while maintaining the high utility of embeddings, indicating that the previous watermarking approach can be breached.In response to this new threat, we propose a new protocol to make the removal of watermarks more challenging by incorporating multiple possible watermark directions.Our defense approach, WARDEN, notably increases the stealthiness of watermarks and has been empirically shown to be effective against CSE attack.