Transformative Advances in Language Models through External Knowledge Integration Author: Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex Author: Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering Author: Wenyi Pi (ORCID: 0009-0002-2884-2771 ) When searching for information on the website, it is common to come across a flood of

Efficient creation of a stoplight report with data dashboard images Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

Unlocking the Power of Questions — A deep dive into Question Answering Systems Author: Amanda Kau (ORCID: 0009–0004–4949–9284 ) Virtual assistants have popped up on numerous websites over the years.

Author Amir Aryani (ORCID: 0000-0002-4259-9774) 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.

An Introduction to RA-CM3, MuRAG and RACE Author Xuzeng He (ORCID: 0009-0005-7317-7426) Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph Author Qingqin Fang (ORCID: 0009–0003–5348–4264) Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Improving the performance of Large Language Models Author Dhruv Gupta (ORCID: 0009-0004-7109-5403) ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

Improving the performance and application of Large Language Models Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.