Large language models (LLMs) have significantly advanced various natural language processing tasks, but they still face substantial challenges in complex mathematical reasoning. The primary problem researchers are trying to solve is how to enable open-source LLMs to effectively handle complex mathematical tasks. Current methodologies struggle with task decomposition for complex problems and fail to provide… →
Due to the complexity of interpreting user questions, database schemas, and SQL production, accurately generating SQL from natural language queries (text-to-SQL) has been a long-standing difficulty. Traditional text-to-SQL systems using deep neural networks and human engineering have succeeded. Then, text-to-SQL jobs were tackled with pre-trained language models (PLMs), and they showed great promise. Problems arise… →
The growth of low-quality data on the internet leads to the instillation of undesirable, unsafe, or toxic knowledge in large language models (LLMs). When these models are used in chatbots, they increase the risk of exposing users to harmful advice or aggressive behavior. Existing toxicity evaluation datasets, primarily focused on English, fail to capture multilingual… →
CONCLUSION: HFNC was not shown to be non-inferior to NIV and resulted in a higher incidence of treatment failure than NIV when used as the initial respiratory support for AECOPD patients with acute-moderate hypercapnic respiratory failure. →
CONCLUSIONS: An intervention of music therapy combined with aerobic exercise can significantly improve the sleep quality of female breast cancer patients undergoing chemotherapy after a radical mastectomy, and this intervention continuously improves many aspects of sleep reactivity. →
BACKGROUND: Strategies to promote mental health care help-seeking among children are needed, especially in low-income and middle-income countries and in complex settings. The aim of this trial was to compare a vignette-based, community-level, proactive case detection tool (CCDT) against standard awareness raising for promoting mental health help-seeking among children and adolescents. →
Survey on Machine Learning-Powered Augmented Reality in Education: ML advances augmented reality (AR) across various educational fields, enhancing object visualizations and interaction capabilities. This survey outlines the integration of ML in AR, discussing its applications from kindergarten to university. It explores ML models like support vector machines, CNNs, and ANNs in AR education. The survey… →
This Paper addresses the limitations of classical machine learning approaches primarily developed for data lying in Euclidean space. Modern machine learning increasingly encounters richly structured data that is inherently non-Euclidean, exhibiting intricate geometric, topological, and algebraic structures. Extracting knowledge from such non-Euclidean data necessitates a broader mathematical perspective beyond the traditional Euclidean framework. Traditional machine… →
Large language models (LLMs), like ChatGPT, are reshaping education by offering new methods for learning and teaching. These advanced models understand and generate human-like text, changing student, educator, and information interaction. LLMs enhance learning efficiency and creativity but raise concerns about trust and potential dependency on technology. The core issue explored in this research is… →
Deepset and Mixedbread have taken a bold step toward addressing the imbalance in the AI landscape that predominantly favors English-speaking markets. They have introduced a groundbreaking open-source German/English embedding model, deepset-mxbai-embed-de-large-v1, to enhance multilingual capabilities in natural language processing (NLP). This model is based on intfloat/multilingual-e5-large and has undergone fine-tuning on over 30 million pairs… →