Large language models (LLMs) are increasingly used to simulate human-like personas for applications in research, education, healthcare, and interactive AI systems. While such persona modeling creates opportunities for interdisciplinary innovation, it raises challenges around authenticity, consistency, bias, and ethical deployment. This workshop brings together perspectives from AI, psychology, cognitive science, and human–computer interaction to advance robust methods, standardized evaluation frameworks, and responsible practices for persona modeling in LLMs. Through invited talks, panels, posters, and discussions, the event will chart a roadmap for interdisciplinary collaboration and future research in this emerging area.

This workshop aims to advance the field of video understanding by fostering discussions around holistic and generalist video foundation models. Building upon the Holistic Video Understanding (HVU) initiative and dataset introduced in 2019, we have successfully organized eight HVU workshops and tutorials at top-tier venues such as CVPR and ICCV, uniting researchers, practitioners, and students from around the world. These efforts have played a central role in moving the community beyond narrow action recognition tasks toward multi-faceted, semantic, and generalist video understanding.With the emergence of large-scale foundation models and video large language models (Video-LLMs), the landscape of video understanding is rapidly evolving. These models enable unified reasoning across spatial, temporal, and multimodal dimensions, yet introduce new challenges in scalability, efficiency, interpretability, and responsible deployment.The HVU Workshop 2025 will provide a platform to explore these frontiers, discussing topics such as multimodal representation learning, long-context reasoning, evaluation of general-purpose video systems, efficient adaptation and scaling laws, and the ethical and societal implications of video AI. Our goal is to bring together a diverse and inclusive community to define the next chapter of holistic, generalist, and responsible video understanding.

Agents have experienced significant growth in recent years, largely due to the rapid technological advancements of Large Language Models (LLMs). Although these agents benefit from LLMs' advanced generation proficiency, they still suffer from catastrophic forgetting and a limited context window size compared to the agents' needs in terms of contextual information. Knowledge Graphs (KGs) are a powerful paradigm for structuring and managing connected pieces of information while unlocking deeper insights than traditional methods. Their value is immense for tasks that require context, integration, and reasoning. However, this power comes at the cost of significant upfront and ongoing investment in construction, curation, and specialized expertise. The first version of this workshop aims at analyzing and discussing emerging and novel practices, ongoing research and validated or deployed innovative solutions that showcase the growing synergy between LLMs agents and KGs.

Large Language Models (LLMs) have transformed NLP research and applications, yet they are still predominantly trained on high-resource, globally dominant languages. This imbalance leads to poor performance and limited applicability for low-resource languages, which are rich in tone, morphology, and cultural meaning. As a result, current AI systems risk reinforcing linguistic inequality, cultural erasure, and lack of accessibility in critical domains like education and healthcare.This workshop aims to reframe language technology by centering low-resource languages, cultures, and epistemologies in the age of LLMs. We seek to bring together researchers, linguists, developers, healthcare professionals, and technologists to share insights and develop strategies for building inclusive, culturally grounded, and linguistically robust language models. The workshop emphasizes collaboration across disciplines and regions to ensure both technical advancement and social relevance.Key areas of focus include developing LLM architectures tailored to low-resource linguistic features, ethical and community-centered dataset collection, and multilingual benchmarks designed specifically for underrepresented languages. We also highlight the importance of healthcare and medical machine translation to support equitable access to information and improve public health outcomes. Ultimately, this workshop aims to advance responsible AI innovation that empowers low-resource language communities and shapes a more inclusive future for global language technologies.

SomosNLP at NeurIPS: Building Principles for Inclusive Spanish & Portuguese NLP This social aims to create a space for academic and technical reflection on the challenges of developing Natural Language Processing (NLP) for languages beyond English, with a special focus on Spanish and Portuguese. We propose a forum to debate methodological, conceptual, and practical tensions, specifically: • How benchmarks and datasets shape progress in non-English NLP. • What biases and omissions today’s AI ecosystem imposes. • What strategies can advance inclusive, multilingual NLP. • How to navigate tensions between industry-driven approaches (fast, large-scale, performance-oriented) and community/academia-driven approaches (careful, linguistically grounded). The event aligns with NeurIPS’ mission by broadening participation, fostering diversity, and enabling global collaboration across academia, industry, and community-driven initiatives.