Online disinformation poses a global challenge, placing significant demands on fact-checkers who must verify claims efficiently to prevent the spread of false information. A major issue in this process is the redundant verification of already fact-checked claims, which increases workload and delays responses to newly emerging claims. This research introduces an approach that retrieves previously fact-checked claims, evaluates their relevance to a given input, and provides supplementary information to support fact-checkers. Our method employs large language models (LLMs) to filter irrelevant fact-checks and generate concise summaries and explanations, enabling fact-checkers to faster assess whether a claim has been verified before. In addition, we evaluate our approach through both automatic and human assessments, where humans interact with the developed tool to review its effectiveness. Our results demonstrate that LLMs are able to filter out many irrelevant fact-checks and, therefore, reduce effort and streamline the fact-checking process.

Access to high-quality medical data is often restricted due to privacy concerns, posing significant challenges for training artificial intelligence (AI) algorithms within Electronic Health Record (EHR) applications. In this study, prompt engineering with the GPT-4 API was employed to generate high-quality synthetic datasets aimed at overcoming this limitation. The generated data encompassed a comprehensive array of patient admission information, including healthcare provider details, hospital departments, wards, bed assignments, patient demographics, emergency contacts, vital signs, immunizations, allergies, medical histories, appointments, hospital visits, laboratory tests, diagnoses, treatment plans, medications, clinical notes, visit logs, discharge summaries, and referrals. To ensure data quality and integrity, advanced validation techniques were implemented utilizing models such as BERT's Next Sentence Prediction for sentence coherence, GPT-2 for overall plausibility, RoBERTa for logical consistency, autoencoders for anomaly detection, and conducted diversity analysis. Synthetic data that met all validation criteria were integrated into a comprehensive PostgreSQL database, serving as the data management system for the EHR application. This approach demonstrates that leveraging generative AI models with rigorous validation can effectively produce high-quality synthetic medical data, facilitating the training of AI algorithms while addressing privacy concerns associated with real patient data.

Red-teaming is a core part of the infrastructure that ensures that AI models do not produce harmful content. Unlike past technologies, the black box nature of generative AI systems necessitates a uniquely interactional mode of testing, one in which individuals on red teams actively interact with the system, leveraging natural language to simulate malicious actors and solicit harmful outputs. This interactional labor done by red teams can result in mental health harms that are uniquely tied to the adversarial engagement strategies necessary to effectively red team. The importance of ensuring that generative AI models do not propagate societal or individual harm is widely recognized -- one less visible foundation of end-to-end AI safety is also the protection of the mental health and wellbeing of those who work to keep model outputs safe. In this paper, we argue that the unmet mental health needs of AI red-teamers is a critical workplace safety concern. Through analyzing the unique mental health impacts associated with the labor done by red teams, we propose potential individual and organizational strategies that could be used to meet these needs, and safeguard the mental health of red-teamers. We develop our proposed strategies through drawing parallels between common red-teaming practices and interactional labor common to other professions (including actors, mental health professionals, conflict photographers, and content moderators), describing how individuals and organizations within these professional spaces safeguard their mental health given similar psychological demands. Drawing on these protective practices, we describe how safeguards could be adapted for the distinct mental health challenges experienced by red teaming organizations as they mitigate emerging technological risks on the new digital frontlines.

In an era increasingly shaped by decentralized knowledge ecosystems and pervasive AI technologies, fostering sustainable learner agency has become a critical educational imperative. This paper introduces a novel conceptual framework integrating Generative Artificial Intelligence (GAI) and Learning Analytics (LA) to cultivate Self - Directed Growth -- a dynamic competency enabling learner s to iteratively drive their own developmental pathways across diverse contexts. Building upon critical gaps in current Self - Directed Learning (SDL) and AI - mediated educational research, the proposed Aspire to Potentials for Learners (A2PL) model reconcept ualizes the interplay of learner aspirations, complex thinking, and summative self - assessment within GAI - supported environments. Methodological implications for future intervention designs and data analytics are discussed, positioning Self - Directed Growth as a pivotal axis for designing equitable, adaptive, and sustainable learning systems in the digital era. 1. Introduction The educational realm faces two increasingly prominent challenges that threaten to reshape the landscape of learning and development . Firstly, the traditional teacher - dominated, institution - centered environment is being eclipsed by a decentralized, ever - evolving, and technologically advanced online landscape. In this new paradigm, knowledge and skills are not poised and delivered by a single expositor, but are constantly renewed, reproduced, and reiterated through sharing and co - creation, rendering existing models of education insufficient. And the overreliance on EdTech tools, as well as information search and synthesis tools, such as Generative Artificial Intelligence (GAI), among students poses a significant challenge in the contemporary educational landscape, while there is a concerning lack of research examining whether these tools genuinely foster the development of learner agency. The integration of AI into educational practices offers a transformative opportunity to enhance learning outcomes and promote equity. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), AI has the potential to acc elerate the achievement of Sustainable Development Goal 4 (SDG 4) by improving access to quality education for all learners, regardless of their socioeconomic background (UNESCO, 2019; UNESCO, 2021). As some noted, AI facilitates access to information and online education, helping to bridge the information, skill, and educational gaps faced by disadvantaged individuals who encounter barriers to traditional learning opportunities due to time constraints, financial limitations, geographic distance, or physic al challenges (Thakkar et al., 2020; Sanabria - Z et al., 2023).

Published as a workshop paper at ICLR 2025DDPS: D iscrete D iffusion P osterior S ampling for Pathsin L ayered G raphs Hao Luan 1, See-Kiong Ng 1,2, and Chun Kai Ling 1 1 School of Computing, National University of Singapore 2 Institute of Data Science, National University of Singapore haoluan@comp.nus.edu.sg, A bstract Di ff usion models form an important class of generative models today, accounting for much of the state of the art in cutting edge AI research. While numerous extensions beyond image and video generation exist, few of such approaches address the issue of explicit constraints in the samples generated. In this paper, we study the problem of generating paths in a layered graph (a variant of a directed acyclic graph) using discrete di ffusion models, while guaranteeing that our generated samples are indeed paths. Our approach utilizes a simple yet e ffective representation for paths which we call the padded adjacency-list matrix (P ALM). In addition, we show how to e ff ectively perform classifier guidance, which helps steer the sampled paths to specific preferred edges without any retraining of the di ff usion model. Our preliminary results show that empirically, our method outperforms alternatives which do not explicitly account for path constraints. 1 I ntroduction Di ffusion models have emerged as one of the most popular methods of generative AI particularly with hyper-realistic image and video generation, often outperforming older methods like generative adversarial networks. The recent years have seen much interest in replicating this success in other domains. These domains include protein design (Frey et al., 2024), molecular conformations (Xu et al., 2022), text generation (Li et al., 2022), robotics (Chi et al., 2023; Wang et al., 2024; Feng et al., 2024; 2025), etc. Unlike image and video generation, These recent applications often require the restriction that the generated samples belong to some discrete domain .

The advent of Large Language Models (LLMs) and generative AI is fundamentally transforming information retrieval and processing on the Internet, bringing both great potential and significant concerns regarding content authenticity and reliability. This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools. Despite their significant impact on the digital ecosystem, these dynamics remain largely uncharted and poorly understood. We propose a stochastic model to characterize the generation, indexing, and dissemination of information in response to new topics. This scenario particularly challenges current LLMs, which often rely on real-time Retrieval-Augmented Generation (RAG) techniques to overcome their static knowledge limitations. Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation across digital resources. An in-depth analysis of Stack Exchange data confirms that high-quality answers inevitably require substantial time and human effort to emerge. This underscores the considerable risks associated with generating persuasive text in response to new questions and highlights the critical need for responsible development and deployment of future generative AI tools.

A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration T asks Khoi Trinh 1, Scott Seidenberger 1, Raveen Wijewickrama 2, Murtuza Jadliwala 2, Anindya Maiti 1 1 University of Oklahoma 2 University of Texas at San Antonio khoitrinh@ou.edu, Abstract With AI-generated content becoming ubiquitous across the web, social media, and other digital platforms, it is vital to examine how such content are inspired and generated. The creation of AIgenerated images often involves refining the input prompt iteratively to achieve desired visual outcomes. This study focuses on the relatively un-derexplored concept of image regeneration using AI, in which a human operator attempts to closely recreate a specific target image by iteratively refining their prompt. Image regeneration is distinct from normal image generation, which lacks any predefined visual reference. A separate challenge lies in determining whether existing image similarity metrics (ISMs) can provide reliable, objective feedback in iterative workflows, given that we do not fully understand if subjective human judgments of similarity align with these metrics. Consequently, we must first validate their alignment with human perception before assessing their potential as a feedback mechanism in the iterative prompt refinement process. To address these research gaps, we present a structured user study evaluating how iterative prompt refinement affects the similarity of regenerated images relative to their targets, while also examining whether ISMs capture the same improvements perceived by human observers. Our findings suggest that incremental prompt adjustments substantially improve alignment, verified through both subjective evaluations and quantitative measures--underscoring the broader potential of iterative workflows to enhance generative AI content creation across various application domains. 1 Introduction The rise of AI-generated content on online platforms has made it crucial to investigate how this type of content is created, specifically through the iterative processes of image generation and regeneration. While prior work has explored AI-led iterative refinement, this paper highlights the human's leading role in refining prompts and improving outcomes through their own judgment and control. The field of generative artificial intelligence (GenAI) has recently seen significant advancements, particularly in the development of text-to-image ( txt2img) models. These models provide an easy and fast process for creating high-quality artwork.

A key feature of human collaboration is the ability to iteratively refine the concepts we have communicated. In contrast, while generative AI excels at the \textit{generation} of content, it often struggles to make specific language-guided \textit{modifications} of its prior outputs. To bridge the gap between how humans and machines perform edits, we present mrCAD, a dataset of multimodal instructions in a communication game. In each game, players created computer aided designs (CADs) and refined them over several rounds to match specific target designs. Only one player, the Designer, could see the target, and they must instruct the other player, the Maker, using text, drawing, or a combination of modalities. mrCAD consists of 6,082 communication games, 15,163 instruction-execution rounds, played between 1,092 pairs of human players. We analyze the dataset and find that generation and refinement instructions differ in their composition of drawing and text. Using the mrCAD task as a benchmark, we find that state-of-the-art VLMs are better at following generation instructions than refinement instructions. These results lay a foundation for analyzing and modeling a multimodal language of refinement that is not represented in previous datasets.

To responsibly develop Generative AI (GenAI) products, it is critical to define the scope of acceptable inputs and outputs. What constitutes a "safe" response is an actively debated question. Academic work puts an outsized focus on evaluating models by themselves for general purpose aspects such as toxicity, bias, and fairness, especially in conversational applications being used by a broad audience. In contrast, less focus is put on considering sociotechnical systems in specialized domains. Yet, those specialized systems can be subject to extensive and well-understood legal and regulatory scrutiny. These product-specific considerations need to be set in industry-specific laws, regulations, and corporate governance requirements. In this paper, we aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy. We compare this taxonomy to existing work in this space and discuss implications of risk category violations on various stakeholders. We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities. Our results demonstrate that these guardrails fail to detect most of the content risks we discuss.

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing.