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.
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.
Improving the performance and application of Large Language Models
An Introduction to RA-CM3, MuRAG, and RACE
Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems
The AI Helper Turning Mountains of Data into Bite-Sized Instructions
Unlocking Knowledge Mining with Machine Learning and Python Integration
Unveiling the Structure Within — Extracting Research Entities and Relationships
Unveiling the Potential of Neo4j and Retrieval-Augmented Generation
A novel approach to improving the efficiency of text search in graph databases utilising Neo4j, OpenAI, and Typesense
