41 flux RSS officiels · actualisés toutes les heures
Ressources · Actualité IA
Laboratoires et éditeurs, presse spécialisée, autorités de régulation : les articles récents réunis en un seul fil. Chaque article s'ouvre sur le site qui l'a publié.
1 flux sur 41 n'ont pas pu être chargés. Les autres sources restent disponibles.
arXiv:2611.10045v1 Announce Type: new Abstract: AI sycophancy, the tendency of large language models (LLMs) to flatter users and affirm their beliefs, can undermine accuracy and contribute to misinformation, dependence, and overconfidence. Although prior work has benchmarked syco…
arXiv:2611.10048v1 Announce Type: new Abstract: Large language models (LLMs) offer a scalable approach for annotating mental health and crisis-related social media content, but their reliability in safety-critical tasks remains unclear. This study evaluates LLM-based crisis-sever…
arXiv:2611.10049v1 Announce Type: new Abstract: LoRA offers a parameter-efficient adaptation strategy, supporting multi-user workloads via customized adapters on a shared backbone. However, serving concurrent requests with different adapters introduces substantial computation and…
arXiv:2611.10054v1 Announce Type: new Abstract: Children and adolescents are increasingly interacting with generative AI and large language models (LLMs), yet little is known about how these interactions may influence their development, beliefs, and behavior. Existing research ha…
arXiv:2611.10089v1 Announce Type: new Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attacks and harmful-content prompts, particularly under adversarial distribution shifts where static alignment pipelines fail to generalize. Although introspective methods …
arXiv:2611.10109v1 Announce Type: new Abstract: The rapid expansion of AI-driven data center construction has outpaced public engagement: an estimated $64 billion in U.S. data center projects were blocked or delayed by local opposition between May 2024 and March 2025, yet the con…
arXiv:2611.10110v1 Announce Type: new Abstract: We study homeopathic learning, a phenomenon where poisoned training data with little effect on its own can create a hidden backdoor once clean, contrasting data is added. In our experiments, a model fine-tuned only on a "poison" dat…
arXiv:2611.10150v1 Announce Type: new Abstract: What is the value of AI-involved art, and to whom does it belong? We begin to answer these questions using data from an in-person, juried AI art exhibition. Our study involved artists, lawyers, and computer scientists (N=209) as eit…
arXiv:2611.09933v1 Announce Type: new Abstract: Large Language Models (LLMs) are emerging as natural-language interfaces for Building Information Modeling (BIM), successfully achieving information retrieval, but no benchmark exists to evaluate their ability to edit Industry Found…
arXiv:2611.09936v1 Announce Type: new Abstract: Natural language inference (NLI) is a fundamental task of natural language processing and crucial for evaluating the strengths and weaknesses of AI methods. Research has mostly focused on encoder or encoder-decoder architectures. Ho…
arXiv:2611.09938v1 Announce Type: new Abstract: LLMs can now draft complete multi-episode dramas, and LLM judges are increasingly used to evaluate them. Assessing the reliability of these judges is difficult because drama quality encompasses specification-backed errors, reproduci…
arXiv:2611.09939v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can memorize and reproduce sensitive information from their training data, posing significant privacy risks. Their ability to encode knowledge across both textual and visual modalities furthe…
