Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Harvesting the Power and Knowledge of Large Language Models Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Boosting Performance for Knowledge Graphs with Neo4j APOC Library Author Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A knowledge graph (graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Understanding Knowledge Networks From A Graph Perspective Author: Amanda Kau (ORCID: 0009–0004–4949–9284 ) Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He ( ORCID: 0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction to OpenAlex In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond Author: Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.