Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. This paper explores the potential of LLMs to shape human perspectives and influence decisions on particular tasks. The researchers investigate using LLMs in persuasion across various domains such as investment, credit cards, insurance, retail, and Behavioral Change Support Systems (BCSS).… →
LAION, a prominent non-profit organization dedicated to advancing machine learning research by developing open and transparent datasets, has recently released Re-LAION 5B. This updated version of the LAION-5B dataset marks a milestone in the organization’s ongoing efforts to ensure the safety and legal compliance of web-scale datasets used in foundational model research. The new dataset… →
In natural language processing (NLP), handling long text sequences effectively is a critical challenge. Traditional transformer models, widely used in large language models (LLMs), excel in many tasks but must be improved when processing lengthy inputs. These limitations primarily stem from the quadratic computational complexity and linear memory costs associated with the attention mechanism used… →
Text-to-image generation has evolved rapidly, with significant contributions from diffusion models, which have revolutionized the field. These models are designed to produce realistic and detailed images based on textual descriptions, which are vital for applications ranging from personalized content creation to artistic endeavors. The ability to precisely control the style of these generated images is… →
Graph learning focuses on developing advanced models capable of analyzing and processing relational data structured as graphs. This field is essential in various domains, including social networks, academic collaborations, transportation systems, and biological networks. As real-world applications of graph-structured data expand, there is an increasing demand for models that can effectively generalize across different graph… →
Cardinality estimation (CE) is crucial in optimizing query performance in relational databases. It involves predicting the number of intermediate results a database query will return, directly influencing the choice of execution plans by query optimizers. Accurate cardinality estimates are essential for selecting efficient join orders, determining whether to use an index and choosing the best… →
Information retrieval (IR) is a fundamental aspect of computer science, focusing on efficiently locating relevant information within large datasets. As data grows exponentially, the need for advanced retrieval systems becomes increasingly critical. These systems use sophisticated algorithms to match user queries with relevant documents or passages. Recent developments in machine learning, particularly in natural language… →
Integrating No-Code AI in Non-Technical Higher Education: Recent developments in ML underscore its ability to drive value across diverse sectors. Nevertheless, incorporating ML into non-technical academic programs, such as those in social sciences, presents challenges due to its usual ties with technical fields like computer science. To overcome this barrier, a case-based approach utilizing no-code… →
Generative AI, an area of artificial intelligence, focuses on creating systems capable of producing human-like text and solving complex reasoning tasks. These models are essential in various applications, including natural language processing. Their primary function is to predict subsequent words in a sequence, generate coherent text, and even solve logical and mathematical problems. However, despite… →