Large Language Models (LLMs) have gained significant attention in data management, with applications spanning data integration, database tuning, query optimization, and data cleaning. However, analyzing unstructured data, especially complex documents, remains challenging in data processing. Recent declarative frameworks designed for LLM-based unstructured data processing focus more on reducing costs than enhancing accuracy. This creates problems… →
The rapid progress of text-to-image (T2I) diffusion models has made it possible to generate highly detailed and accurate images from text inputs. However, as the length of the input text increases, current encoding methods, such as CLIP (Contrastive Language-Image Pretraining), encounter various limitations. These methods struggle to capture the full complexity of long text descriptions,… →
As large language models (LLMs) become increasingly capable and better day by day, their safety has become a critical topic for research. To create a safe model, model providers usually pre-define a policy or a set of rules. These rules help to ensure the model follows a fixed set of principles, resulting in a model… →
CONCLUSIONS: App-based lifestyle interventions for GDM management are not common in LMICs, where GDM prevalence is rapidly increasing. This proof-of-concept trial will provide valuable insights into the potential of leveraging mHealth app-based platforms for GDM self-management in LMICs. →
CONCLUSIONS: In this study, we found that a self-guided digital family support intervention initiated at the time of a child’s T1D diagnosis was largely feasible and acceptable. Overall, rates of participation and module completion were similar to or higher than other self-guided digital prevention interventions for mental and physical health outcomes. Self-guided digital programs addressing… →
CONCLUSION: We confirmed and extended earlier research on PSM. Our study involved a specific pain population receiving a nonpharmacological intervention. Our results highlight the importance of targeting PSM and anxiety within a treatment to take measures to mitigate the prevalence of side effects. →
Accelerating inference in large language models (LLMs) is challenging due to their high computational and memory requirements, leading to significant financial and energy costs. Current solutions, such as sparsity, quantization, or pruning, often require specialized hardware or result in decreased model accuracy, making efficient deployment difficult. Researchers from FAIR at Meta, GenAI at Meta, Reality… →
Proteins, vital macromolecules, are characterized by their amino acid sequences, which dictate their three-dimensional structures and functions in living organisms. Effective generative protein modeling requires a multimodal approach to simultaneously understand and generate sequences and structures. Current methods often rely on separate models for each modality, limiting their effectiveness. While advancements like diffusion models and… →
Large language models (LLMs) can understand and generate human-like text across various applications. However, despite their success, LLMs often need help in mathematical reasoning, especially when solving complex problems requiring logical, step-by-step thinking. This research field is evolving rapidly as AI researchers explore new methods to enhance LLMs’ capabilities in handling advanced reasoning tasks, particularly… →
Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. However, their limitation lies with long-term planning and complex problem-solving. While explicit search methods like Monte Carlo Tree Search (MCTS) have been employed to enhance decision-making in various AI systems, including chess engines and game-playing algorithms, they present challenges… →