Machine learning focuses on developing models that can learn from large datasets to improve their predictions and decision-making abilities. One of the core areas of development within machine learning is neural networks, which are especially critical for tasks such as image recognition, language processing, and autonomous decision-making. These models are governed by scaling laws, suggesting…
The need for efficient and trustworthy techniques to assess the performance of Large Language Models (LLMs) is increasing as these models are incorporated into more and more domains. When evaluating how effectively LLMs operate in dynamic, real-world interactions, traditional assessment standards are frequently used on static datasets, which present serious issues. Since the questions and…
Model merging, particularly within the realm of large language models (LLMs), presents an intriguing challenge that addresses the growing demand for versatile AI systems. These models often possess specialized capabilities such as multilingual proficiency or domain-specific expertise, making their integration crucial for creating more robust, multi-functional systems. However, merging LLMs effectively is not trivial; it…
Large language models (LLMs) have revolutionized various fields by enabling more effective data processing, complex problem-solving, and natural language understanding. One major innovation is retrieval-augmented generation (RAG), which allows LLMs to retrieve relevant information from external sources, such as large knowledge databases, to generate better answers. However, the integration of long-context LLMs with RAG presents…
High-performance AI models that can run at the edge and on personal devices are needed to overcome the limitations of existing large-scale models. These models require significant computational resources, making them dependent on cloud environments, which poses privacy risks, increases latency, and adds costs. Additionally, cloud reliance is not suitable for offline scenarios. Introducing Ministral…
Large language models (LLMs) have gained widespread adoption due to their advanced text understanding and generation capabilities. However, ensuring their responsible behavior through safety alignment has become a critical challenge. Jailbreak attacks have emerged as a significant threat, using carefully crafted prompts to bypass safety measures and elicit harmful, discriminatory, violent, or sensitive content from…
The most serious challenge regarding IGNNs relates to slow inference speed and scalability. While these networks are effective at capturing long-range dependencies in graphs and addressing over-smoothing issues, they require computationally expensive fixed-point iterations. This reliance on iterative procedures severely limits their scalability, particularly when applied to large-scale graphs, such as those in social networks,…
Reinforcement learning (RL) provides a framework for learning behaviors for control and making decisions (known as policies) that help the model earn the most rewards in a given environment. Online RL algorithms iteratively take actions, collecting observations and rewards from the environment, and then update their policy using the latest experience. This online learning process…
Quantum computers are a revolutionary technology that harnesses the principles of quantum mechanics to perform calculations that would be infeasible for classical computers. Evaluating the performance of quantum computers has been a challenging task due to their sensitivity to noise, the complexity of quantum algorithms, and the limited availability of powerful quantum hardware. Decoherence and…