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.

How to efficiently retrieve information for different applications Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Introduction 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. Many of us have since learnt — perhaps when asking ChatGPT a question or employing it to write our reports — that LLMs can hallucinate.

Introduction Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Introduction Large Language Models (LLMs) are artificial intelligence (AI) models that are trained on massive amounts of text data in order to generate human-like language and produce coherent and contextually relevant responses. These models have significantly advanced natural language processing tasks such as text generation, translation, sentiment analysis, text-to-image generation, image captioning, and much more.

Introduction This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered. Furthermore, the article will delve into how these methods work within dialogue systems.

Introduction Large Language Models (LLMs) have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them. However, there are some challenges, such as limited prompt size, and limited context windows that make LLMs not suitable for some.

Introduction GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

Introduction Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data. Currently, the publications are available as PDF files, and due to the free-form text, it is difficult to parse a PDF file to extract structured information.

Introduction Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information. With large amounts of data, it sometimes becomes difficult to draw insights from it. This blog article examines how to combine Neo4j, a graph database, with OpenAI’s Retrieval-Augmented Generation (RAG) model to build a robust knowledge management system.

Introduction The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology. When it comes to natural language processing, search capabilities, and graph databases, LLM (Large Language Models), Typesense , and Neo4j work together as a powerful trio that offers a cohesive and effective solution.