Introduction Large language models (LLMs) like ChatGPT have transformed how we use artificial intelligence, excelling at tasks like writing essays, answering questions, and even holding conversations. But when it comes to complex reasoning—think solving math problems, tackling commonsense puzzles, or working with symbols—these models often hit a wall.

Introduction The rapid advancement of Large Language Models (LLMs) has revolutionised artificial intelligence, enabling powerful text understanding and generation capabilities. However, despite their strengths, LLMs often struggle with complex reasoning tasks requiring deep abstraction. A new paradigm known as Meta Prompting (MP) emerges as a promising technique to enhance LLM efficiency and cognitive depth.

The Evolution of Numerical Reasoning in AI: The Power of Program of Thoughts (PoT) Introduction Artificial Intelligence has made significant strides in solving complex numerical reasoning tasks. Traditional approaches focused on training models from scratch or fine-tuning them to demonstrate reasoning step by step. However, these methods often required extensive datasets and training with manually crafted reasoning steps.

Introduction In the rapidly evolving field of natural language processing (NLP), In-Context Learning (ICL) has emerged as a transformative capability of large language models (LLMs). The paper "A Survey on In-Context Learning" by Dong et al. (2024) offers a comprehensive exploration of this paradigm, which enables LLMs to adapt to tasks using only a few examples provided in the input prompt [1]. This survey is a

Introduction As Large Language Models (LLMs) become increasingly integrated into our digital infrastructure, their remarkable ability to generate human-like text comes with a significant challenge: hallucination - the generation of plausible yet factually incorrect information. This phenomenon poses a particular concern in contexts where accuracy is paramount, from research analysis to decision-making systems.

Introduction AI-generated images have become an important tool in the design field, but how to naturally merge people into the landscape background to make the picture look realistic and artistic has always been a challenge. From movie special effects to social media content creation, the technology of seamless synthesis is becoming more and more important.

Getting Started In today’s AI-driven world, running Large Language Models (LLMs) on your own computer has never been easier, thanks to LM Studio. Whether you’re a developer, researcher, or just an AI enthusiast, LM Studio provides a seamless way to download, manage, and interact with open-source models—without relying on cloud-based APIs.

Introduction This article is based on the blog Introducing the PID Graph published on DataCite. In the field of scientific research, every discovery, paper, and dataset is an important part of the knowledge base.

Introduction In recent years, Large Language Models (LLMs) have demonstrated remarkable performance across a variety of natural language processing tasks. However, their ability to perform complex reasoning remains a key challenge. Unlike humans, who can reason step by step and learn from both correct and incorrect examples, LLMs often struggle with logical consistency and generalisation beyond seen data.

Introduction In this article, we look at the graph database developed as part of the National Graph project and how to work with it using Cypher queries. As of April 24, 2024, the Australian National Graph, the graph database developed as part of this project, included data on 259,111 Australian researchers, 27,566 datasets, 1,764,309 publications, 88,234 grants, and 4,406 Australian organisations.

Introduction In this article, we look at Research Graph as an information model, and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.