Multimodal language models represent an emerging field in artificial intelligence that aims to enhance machine understanding of text and images. These models integrate visual and textual information to interpret and reason through complex data. Their capabilities span beyond simple text comprehension, pushing artificial intelligence toward more sophisticated realms where machine learning interacts seamlessly with the… →

Natural language processing (NLP) focuses on enabling computers to understand and generate human language, making interactions more intuitive and efficient. Recent developments in this field have significantly impacted machine translation, chatbots, and automated text analysis. The need for machines to comprehend large amounts of text and provide accurate responses has led to the development of… →

Artificial Intelligence (AI) is changing the world quickly as several nations and international organizations have adopted frameworks to direct the development, application, and governance of AI. Numerous initiatives are influencing the ethical use of AI to prioritize human rights and innovation. Here are some of the top AI governance laws and frameworks.  1. EU AI… →

Large Language Models (LLMs) often provide confident answers, raising concerns about their reliability, especially for factual questions. Despite widespread hallucination in LLM-generated content, no established method to assess response trustworthiness exists. Users lack a “trustworthiness score” to determine response reliability without further research or verification. The aim is for LLMs to yield predominantly high trust… →

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Despite their importance affirmed by the universal approximation theorem, they possess drawbacks. In applications like transformers, MLPs often monopolize parameters and lack interpretability compared to attention layers. While exploring alternatives, such as the Kolmogorov-Arnold… →

Iterative preference optimization methods have shown efficacy in general instruction tuning tasks but yield limited improvements in reasoning tasks. These methods, utilizing preference optimization, enhance language model alignment with human requirements compared to sole supervised fine-tuning. Offline techniques like DPO are gaining popularity due to their simplicity and efficiency. Recent advancements advocate the iterative application… →