相关论文: Conflect: Designing Reflective Thinking-Based Cont…
Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substantial privacy risks, as unauthorized access may expose personal, medical, or legal…
Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some…
For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present…
To account for privacy perceptions and preferences in user models and develop personalized privacy systems, we need to understand how users make privacy decisions in various contexts. Existing studies of privacy perceptions and behavior…
Since the introduction of the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), software developers increasingly have to make privacy-related decisions during system design and…
We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which identifies relevant evidence from context, and reasoning are deeply intertwined: retrieval…
The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances…
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to…
As language models evolve into autonomous agents that act and communicate on behalf of users, ensuring safety in multi-agent ecosystems becomes a central challenge. Interactions between personal assistants and external service providers…
With large language models (LLMs) increasingly deployed as cognitive engines for AI agents, the reliability and effectiveness critically hinge on their intrinsic epistemic agency, which remains understudied. Epistemic agency, the ability to…
With the rapid growth of the internet, all online activities can have both positive and negative effects on human mental health. Online engagement is complex and efforts to regulate online use face challenges in distinguishing between…
Organizations started to adopt differential privacy (DP) techniques hoping to persuade more users to share personal data with them. However, many users do not understand DP techniques, thus may not be willing to share. Previous research…
The increasing concern in user privacy misuse has accelerated research into checking consistencies between smartphone apps' declared privacy policies and their actual behaviors. Recent advances in Large Language Models (LLMs) have…
With the increase in the number of privacy regulations, small development teams are forced to make privacy decisions on their own. In this paper, we conduct a mixed-method survey study, including statistical and qualitative analysis, to…
As Large Language Models (LLMs) are increasingly deployed in sensitive domains such as enterprise and government, ensuring that they adhere to user-defined security policies within context is critical-especially with respect to information…
Recent improvement gains in large language models (LLMs) have lead to everyday usage of AI-based Conversational Agents (CAs). At the same time, LLMs are vulnerable to an array of threats, including jailbreaks and, for example, causing…
Digital privacy notices aim to provide users with information to make informed decisions. They are, however, fraught with difficulties. Instead, I propose that data privacy decisions can be understood as an expression of user values. To…
Context. Refining or altering existing behavior is the daily work of every developer, but that cannot be always anticipated, and software sometimes cannot be stopped. In such cases, unanticipated adaptation of running systems is of interest…
Expressing stressful experiences in words is proven to improve mental and physical health, but individuals often disengage with writing interventions as they struggle to organize their thoughts and emotions. Reflective prompts have been…
Ineffective meetings are pervasive. Thinking ahead explicitly about meeting goals may improve effectiveness, but current collaboration platforms lack integrated support. We tested a lightweight goal-reflection intervention in a…